A system and method for monitoring parameters of a workpiece for cycloidal dividing machining

Abstract

The application relates to the field of mechanical processing, in particular to a workpiece parameter monitoring system and method for cycloidal rotation division processing, which comprises the following steps: collecting temperature data of a cutter and load data of a driving part in a cycloidal rotation division processing process in real time; collecting historical processing data when workpiece errors exist under various processing parameters; extracting the temperature data of the cutter, the load data of the driving part and corresponding workpiece errors from the historical processing data; establishing a relationship function of the processing parameters, error temperature change intervals, error load change intervals and the workpiece errors; obtaining real-time processing parameters and verifying real-time temperature change intervals and real-time load change intervals based on the relationship function; and judging the workpiece errors. According to the application, the temperature and load data of the cutter are collected based on the cycloidal rotation division processing principle, and the workpiece parameter errors can be monitored in real time.

Description

Technical Field

[0001] This invention relates to the field of machining, and specifically to a workpiece parameter monitoring system and method for cycloidal rotary machining. Background Technology

[0002] Cycloidal rotary machining is an advanced machining process that utilizes the geometric properties of cycloids to achieve efficient and precise machining of workpieces. A cycloid is the trajectory formed by a moving point on a plane rolling purely around a fixed circle. When the moving circle rolls inside the fixed circle, an incycloid is generated; when it rolls outside the fixed circle, an excycloid is generated. In engineering, the moving circle is called the generating circle, and the fixed circle is called the base circle.

[0003] The principle of cycloidal rotary machining is to make the trajectory of the tool tip follow a cycloidal path. This method utilizes the characteristic of the cycloidal trajectory being periodically distributed along a circumference to efficiently machine parts with circumferentially distributed surfaces, such as gear chamfers and gear sleeve tapered surfaces. During machining, the workpiece axis coincides with the base circle axis, and the tool axis coincides with the producing circle axis, forming a cycloidal trajectory for the tool tip. The tool has two types of motion: one is its rotation around its own axis; the other is its revolution around the workpiece axis. These two motions are achieved by the synchronous rotation of the workpiece axis and the tool axis at a specific speed ratio.

[0004] By monitoring workpiece parameters during machining, the machining error of the workpiece can be analyzed in real time. Further analysis of the machining error can assess the cycloidal spin-machining status and optimize process parameters. For example, by obtaining the error of the workpiece under the current cutting parameters, the tool axis speed, workpiece axis speed, feed rate, etc., can be optimized to improve machining efficiency and quality.

[0005] Existing workpiece parameter monitoring technologies, such as the cloud-based industrial manufacturing system and its control method disclosed in CN114637261A, obtain workpiece parameters during processing by analyzing collected workpiece image data. However, this method is only suitable for machining techniques involving intermittent or low-speed rotation of the workpiece, such as traditional indexing. In cycloidal rotary indexing, the workpiece rotates at a considerable height, making it difficult to acquire clear workpiece images for parameter acquisition. Therefore, a workpiece parameter monitoring process adapted to cycloidal rotary indexing is needed to obtain real-time information on workpiece errors during the machining process. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a workpiece parameter monitoring system and method for cycloidal rotary machining, which obtains the machining error of the workpiece during the machining process by observing the periodic patterns of tool temperature and drive load.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] On the one hand, a method for monitoring workpiece parameters in cycloidal spinning is provided, including: real-time acquisition of tool temperature data and drive load data during cycloidal spinning, defined as real-time temperature data and real-time load data; real-time temperature data includes real-time temperature change interval, and real-time load data includes real-time load change interval;

[0009] Collect historical machining data when workpiece errors exist under various machining parameters, and extract tool temperature data, drive load data, and corresponding workpiece errors from the historical machining data. Tool temperature data and drive load data are defined as error temperature data and error load data, respectively. Error temperature data includes the error temperature change interval, and error load data includes the error load change interval.

[0010] Establish a relationship function between processing parameters, error temperature change interval, error load change interval and workpiece error, obtain real-time processing parameters and verify the real-time temperature change interval and real-time load change interval based on the relationship function, and judge the workpiece error.

[0011] Furthermore, the real-time temperature data is acquired through a temperature sensor, and the real-time load data is acquired through a current sensor;

[0012] The real-time temperature change interval is the interval between two consecutive temperature abrupt change points collected in real time, and the real-time load change interval is the interval between two consecutive load abrupt change points collected in real time.

[0013] Furthermore, a real-time temperature waveform is generated based on the temperature data collected by the temperature sensor, and a real-time load waveform is generated based on the current data collected by the current sensor; the wavelength of the real-time temperature waveform is the real-time temperature change interval, and the wavelength of the real-time load waveform is the real-time load change interval.

[0014] Furthermore, when the real-time temperature change interval is synchronized with the real-time load change interval, the real-time temperature change interval and the real-time load change interval are verified based on the relational function to determine the workpiece error; when the real-time temperature change interval and the real-time load change interval are not synchronized, the workpiece error is directly determined.

[0015] Furthermore, the real-time temperature data also includes the duration of real-time temperature changes, and the error temperature data also includes the duration of error temperature changes. A relationship function is established between the processing parameters, the error temperature change interval, the error temperature change duration, the error load change interval, and the workpiece error. Real-time processing parameters are obtained, and the real-time temperature change interval, the real-time temperature change duration, and the real-time load change interval are verified based on the relationship function to determine the workpiece error.

[0016] Furthermore, workpiece error includes the location where the workpiece error occurs and the amount of workpiece error.

[0017] Furthermore, based on the relational function, the time and amount of workpiece error are output, and the location of workpiece error is determined based on the tool tip motion trajectory and the time of workpiece error occurrence.

[0018] Furthermore, the formula for the movement trajectory of the tool tip is as follows:

[0019]

[0020] Where R is the base circle radius, e is the distance from the fixed point to the center of the generating circle, β is the initial angle, α is the revolution angle, z is the number of workpiece teeth, and k is the number of teeth spanned.

[0021] Furthermore, the real-time temperature data also includes the real-time temperature change, and the error temperature data also includes the error temperature change.

[0022] Real-time load data also includes the time of real-time load change. The real-time temperature change segment is extracted with the time of real-time load change as the center. The temperature change in the real-time temperature change segment is the real-time temperature change.

[0023] Error load data also includes the time of error load change. The error temperature change segment is extracted with the time of error load change as the center, and the temperature change in the error temperature change segment is the error temperature change.

[0024] Based on real-time temperature changes and error temperature changes, the trend of workpiece error occurrence is predicted.

[0025] On the other hand, a workpiece parameter monitoring system for cycloidal spinning is provided, comprising:

[0026] The data acquisition module is used to collect the temperature data of the tool and the load data of the drive component in real time during the cycloidal spinning process. It is defined as real-time temperature data and real-time load data. The real-time temperature data includes the real-time temperature change interval, and the real-time load data includes the real-time load change interval.

[0027] The data recording module is used to collect historical machining data when workpiece errors exist under various machining parameters. It extracts tool temperature data, drive load data, and corresponding workpiece errors from the historical machining data. Tool temperature data and drive load data are defined as error temperature data and error load data, respectively. Error temperature data includes the error temperature change interval, and error load data includes the error load change interval.

[0028] The parameter analysis module is used to establish the relationship function between processing parameters, error temperature change interval, error load change interval and workpiece error, obtain real-time processing parameters and verify the real-time temperature change interval and real-time load change interval based on the relationship function, and judge the workpiece error.

[0029] Beneficial Effects: 1. This solution is applicable to cycloidal rotary machining. During cycloidal rotary machining, the tool and workpiece rotate at a certain speed ratio. Since both are in a rotating state, the tool intermittently cuts each tooth of the workpiece. When the number of rotations of the tool is the same as the number of teeth of the workpiece, one cut is completed for each tooth. The tool feeds to complete multiple cuts for each tooth, ultimately completing the machining of the entire workpiece tooth. Because the tool intermittently contacts the workpiece teeth, the load of the drive component changes abruptly at each contact moment and then returns to normal. The temperature of the tool tip in contact with the workpiece begins to rise until the tool tip separates from the workpiece. Under the same machining parameters, by monitoring the real-time temperature change interval and the real-time load change interval, and by using historical machining data when workpiece errors exist under each machining parameter, it is possible to determine whether there is an error in the workpiece at the current moment. Compared with existing technologies, based on the principle of cycloidal rotary machining, it is possible to monitor abnormal workpiece parameters in real time.

[0030] 2. Based on the tool tip motion trajectory, the tool tip position at each moment after the start of cycloidal rotation machining can be calculated. Furthermore, based on the moment the workpiece error occurs, the tool tip position at that moment can be obtained, thus accurately outputting the position of the workpiece teeth with errors. It can also monitor real-time temperature and load change intervals and, using historical machining data under various machining parameters when workpiece errors exist, determine the current error magnitude. Compared to existing technologies, this method can output the specific machining error tooth position and error magnitude of the workpiece in real time.

[0031] 3. By extracting the temperature change before and after each contact between the tool and the workpiece (load abrupt change), since precision machining is usually performed at a constant temperature, the temperature change should be similar across different machining parameters when no machining error occurs. When the temperature change gradually changes, it reflects an abnormal temperature change in the tool due to a gradual change in the cutting position or depth of cut, thus predicting the trend of workpiece error. Compared to existing technologies, this method can predict workpiece parameter errors and, to some extent, avoid their occurrence. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of cycloidal spinning machining, representing an embodiment of the workpiece parameter monitoring method for cycloidal spinning machining according to the present invention.

[0033] Figure 2 This is a schematic diagram illustrating the steps of an embodiment of the workpiece parameter monitoring method for cycloidal spinning of the present invention;

[0034] Figure 3 This is a schematic diagram of a module of an embodiment of the workpiece parameter monitoring system for cycloidal spinning of the present invention.

[0035] The reference numerals in the accompanying drawings include: 1. cutting tool; 2. workpiece; 3. temperature sensor; 4. driving component. Detailed Implementation

[0036] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0037] The following detailed description illustrates the specific implementation method:

[0038] Implementation, for example, attached Figure 1 and Figure 2 As shown: A method for monitoring two parameters of a workpiece used in cycloidal spinning, including:

[0039] S1 Real-time Data Acquisition: This involves real-time acquisition of the temperature data of the tool 1 and the load data of the drive component 4 during the cycloidal spinning process, defined as real-time temperature data and real-time load data. In this embodiment, the acquired real-time temperature data and real-time load data serve as the basis for monitoring the parameters of the workpiece 2. Based on the cycloidal spinning principle, the tool 1 intermittently contacts and cuts the workpiece 2 during the cycloidal spinning process, and the contact frequency is stable. The temperature data and load data of the tool 1 differ from those of traditional continuous cutting. In continuous cutting, the temperature data of the tool 1 increases linearly and remains at a certain temperature, while the load data is relatively stable. However, in cycloidal spinning, the temperature data of the tool 1 increases in a stepwise manner, with a certain temperature drop after each increase. After reaching a certain temperature, it fluctuates with the contact between the tool 1 and the workpiece 2. The load data drops sharply at the beginning of each contact cutting phase and then returns to normal stable operation.

[0040] In this embodiment, the real-time temperature data includes the real-time temperature change interval, the real-time temperature change duration, and the real-time temperature change amount. The real-time temperature change interval is used to verify whether and when the parameter error of workpiece 2 occurs. The real-time temperature change duration assists in verifying whether and when the parameter error of workpiece 2 occurs. The real-time temperature change amount is used to predict the area where the parameter error of workpiece 2 will occur in the future. The real-time load data includes the real-time load change interval and the real-time load change time. The real-time load change interval is used to verify whether and when the parameter error of workpiece 2 occurs. The real-time load change time is used to obtain the contact time between the tool 1 and the workpiece 2.

[0041] In this step, real-time temperature data is acquired through temperature sensor 3, and real-time load data is acquired through current sensor. Temperature sensor 3 can be fixed on the tool 1 at any position that does not affect cutting. Since the tip of the tool 1 is in contact with the workpiece 2 during cutting, a non-contact temperature sensor 3 is preferred. The temperature sensor 3 is mounted on the tool shaft or tool holder, with its detection end aligned with the tool tip to detect the temperature of the tool tip.

[0042] S2 Historical Data Acquisition: Collect historical machining data when there is workpiece 2 error under various machining parameters (workpiece 2 parameters, tool 1 parameters, feed parameters, etc.). Extract the temperature data of tool 1 and the load data of drive 4, as well as the corresponding workpiece 2 error, from the historical machining data. The temperature data of tool 1 and the load data of drive 4 are defined as error temperature data and error load data.

[0043] Corresponding to S1, the error temperature data includes the error temperature change interval, the duration of the error temperature change, and the amount of error temperature change. The error load data includes the error load change interval and the time of the error load change. The function of each parameter also corresponds to S1.

[0044] The real-time temperature change interval in S1 and S2 is the interval between two consecutive temperature abrupt change points in real-time data acquisition, and the real-time load change interval is the interval between two consecutive load abrupt change points in real-time data acquisition. A real-time temperature waveform can be generated from the temperature data acquired by temperature sensor 3, and a real-time load waveform can be generated from the current data acquired by current sensor; the wavelength of the real-time temperature waveform is the real-time temperature change interval, and the wavelength of the real-time load waveform is the real-time load change interval.

[0045] S3 Workpiece Parameter Prediction: Based on the aforementioned machining parameters, workpiece 2 error, error temperature change interval, error temperature change duration, and error load change interval, a corresponding relational function can be established. Using this relational function for modeling, and by acquiring real-time machining parameters and verifying the real-time temperature change interval, real-time temperature change duration, and real-time load change interval based on the relational function, it is possible to predict whether workpiece 2 parameter error will occur. The prediction result also includes the time when the workpiece 2 parameter error occurs.

[0046] In some embodiments, a BP neural network is constructed and trained to predict the error of workpiece 2 parameters. The BP neural network is trained using the processing parameters, workpiece 2 error, error temperature change interval, error temperature change duration, and error load change interval in historical processing data as training samples. The trained BP neural network can directly predict whether the error of workpiece 2 parameters occurs and the time when the error of workpiece 2 parameters occurs by receiving the real-time temperature change interval, real-time temperature change duration, and real-time load change interval.

[0047] In some embodiments, before predicting whether workpiece 2 parameter errors occur and the timing of their occurrence through the aforementioned relational function modeling or BP neural network, a preliminary judgment of workpiece 2 parameter errors is made by checking whether the real-time temperature change interval and the real-time load change interval are synchronized. If the real-time temperature change interval and the real-time load change interval are synchronized, it indicates that the workpiece 2 axis is operating according to preset parameters without interfering with the cutting of tool 1, and therefore the workpiece 2 parameter error judgment continues based on relational function modeling or BP neural network. If the real-time temperature change interval and the real-time load change interval are not synchronized, it indicates that the workpiece 2 axis is not operating according to preset parameters and is interfering with the cutting of tool 1, and workpiece 2 parameters have already experienced errors, therefore the workpiece 2 error is directly judged.

[0048] In some embodiments, the location of the error in workpiece 2 and the amount of error in workpiece 2 are also predicted, including:

[0049] The time when the error of workpiece 2 occurs and the amount of error of workpiece 2 are output based on the relational function or the trained BP neural network. The time when the error of workpiece 2 occurs is the time when the error of workpiece 2 is predicted to exist by the relational function or the trained BP neural network. The amount of error of workpiece 2 is predicted and output by the relational function or the trained BP neural network based on the work parameters, the interval of error temperature change, the interval of error load change, the duration of error temperature change, and the corresponding amount of error of workpiece 2.

[0050] Based on the formula for the motion trajectory of the tool tip of tool 1, the motion trajectory of the tool tip of tool 1 is obtained:

[0051]

[0052] Where R is the base circle radius, e is the distance from the fixed point to the center of the generating circle, β is the initial angle, α is the revolution angle, z is the number of teeth on the workpiece, and k is the number of teeth spanned. k satisfies the relationship r / R = k / z, where r is the generating circle radius and k is a natural number less than z.

[0053] After the machining begins, the tip of the cutting tool 1 contacts the corresponding teeth of the workpiece 2 sequentially according to the motion trajectory. Therefore, by obtaining the time when the error of the workpiece 2 occurs, the location of the error in the workpiece 2 can be determined.

[0054] In some embodiments, the collected temperature changes are used to predict the error trend of workpiece 2 through a relational function or by training a backpropagation neural network. The aforementioned temperature changes refer to the temperature changes within a preset time period before and after the tool 1 contacts the workpiece 2. For the same processing parameters, the temperature changes should be similar, with minimal deviation. Therefore, the error trend of workpiece 2 is predicted based on the temperature changes during each contact between the tool 1 and the workpiece 2.

[0055] First, a real-time temperature change segment is extracted centered on the moment of real-time load change, and the temperature change within this segment is taken as the real-time temperature change. Similarly, an error temperature change segment is extracted centered on the moment of error load change, and the temperature change within this segment is taken as the error temperature change. The error temperature change is then used as a parameter of a relational function or as a training sample for a backpropagation neural network. Based on the collected real-time temperature changes, the error trend of workpiece 2 is output in real time.

[0056] In some embodiments, a corresponding monitoring system is constructed based on the above-described method for monitoring the parameters of workpiece 2 in cycloidal spin machining, with the structure as follows: Figure 3 As shown, by integrating the system into the existing cycloidal spinning processing equipment, the location of the error in workpiece 2, the amount of error in workpiece 2, and the trend of error occurrence in workpiece 2 can be obtained and output through the cycloidal spinning processing equipment.

[0057] Based on the aforementioned workpiece parameter monitoring method and system, chamfering monitoring was performed on carburized and quenched gears. Gear parameters: module m = 2mm, number of teeth z = 47, pressure angle 20°, tooth width 18mm. An existing cycloidal rotary milling machine was used, with a maximum cutter spindle speed of 3000 r / min and a maximum workpiece axis speed of 200 r / min. Process requirements: chamfer width 0.15mm ± 0.05mm, chamfer angle 45° ± 3°.

[0058] Temperature sensor 3 is an OPTRISCTlaser 1M infrared fiber optic thermometer with a range of 250℃~2200℃ and a response time of 1ms. It is installed on the side of the tool holder with a spot diameter of 0.7mm, aligned with the tip of tool 1 at a distance of 80mm.

[0059] The current sensor selected is the LEMIT400-S Hall current sensor, with a range of ±400A and a bandwidth of 100kHz, which is connected to the power line of the cutter shaft servo motor.

[0060] Using an existing industrial control computer, temperature data and load data collected by temperature sensor 3 and current sensor are acquired via a data acquisition card; an offline-trained BP neural network is integrated into the industrial control computer, and the real-time CNC parameters (speed, feed) of the machine tool are input on the industrial control computer.

[0061] During online monitoring, real-time data is collected: the sampling rate is 10kHz, and peak values ​​of the temperature and current waveforms are detected every 0.1s. Parameters such as the time interval between the current temperature peak and the previous peak, and the time interval between the load change and the previous drop are calculated in real time. The error status of workpiece 2 parameters and the error trend warning for workpiece 2 are output through a BP neural network.

[0062] After continuously performing chamfering on 200 gears, the system detected errors in 9 pieces. Among these, 7 pieces were confirmed by laser scanner to have an error E > 0.05 mm, achieving a monitoring accuracy of 97.5%. Two pieces were missed, resulting in a miss rate of 1%. There were no over-detections. The system delay was <0.8 seconds, meeting the online cycle time requirement (35 seconds per piece).

[0063] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for monitoring workpiece parameters in cycloidal spinning, characterized in that, include: The temperature data of the tool and the load data of the drive component are collected in real time during the cycloidal spinning process and defined as real-time temperature data and real-time load data; the real-time temperature data includes the real-time temperature change interval and the real-time load data includes the real-time load change interval. The real-time temperature data is collected by a temperature sensor, and the real-time load data is collected by a current sensor; the real-time temperature change interval is the interval between two consecutive temperature abrupt change points in real-time data collection, and the real-time load change interval is the interval between two consecutive load abrupt change points in real-time data collection. A real-time temperature waveform is generated based on temperature data collected by a temperature sensor, and a real-time load waveform is generated based on current data collected by a current sensor; the wavelength of the real-time temperature waveform is the real-time temperature change interval, and the wavelength of the real-time load waveform is the real-time load change interval. Collect historical machining data when workpiece errors exist under various machining parameters, and extract tool temperature data, drive load data, and corresponding workpiece errors from the historical machining data. Tool temperature data and drive load data are defined as error temperature data and error load data, respectively. Error temperature data includes the error temperature change interval, and error load data includes the error load change interval. Establish a relationship function between processing parameters, error temperature change interval, error load change interval and workpiece error, obtain real-time processing parameters and verify the real-time temperature change interval and real-time load change interval based on the relationship function, and judge the workpiece error.

2. The workpiece parameter monitoring method for cycloidal spinning according to claim 1, characterized in that, When the real-time temperature change interval is synchronized with the real-time load change interval, the real-time temperature change interval and the real-time load change interval are verified based on the relational function to determine the workpiece error; when the real-time temperature change interval and the real-time load change interval are not synchronized, the workpiece error is directly determined.

3. The method for monitoring workpiece parameters in cycloidal spinning according to claim 1, characterized in that, Real-time temperature data also includes the duration of real-time temperature changes, and error temperature data also includes the duration of error temperature changes. A relationship function is established between processing parameters, error temperature change interval, error temperature change duration, error load change interval, and workpiece error. Real-time processing parameters are obtained, and the real-time temperature change interval, real-time temperature change duration, and real-time load change interval are verified based on the relationship function to determine the workpiece error.

4. The method for monitoring workpiece parameters in cycloidal spinning according to claim 1, characterized in that, Workpiece error includes the location of the error and the amount of the error.

5. The method for monitoring workpiece parameters in cycloidal spinning according to claim 4, characterized in that, The time and amount of workpiece error are output based on the relational function, and the location of workpiece error is determined based on the tool tip motion trajectory and the time of workpiece error occurrence.

6. The method for monitoring workpiece parameters in cycloidal spinning according to claim 1, characterized in that, Real-time temperature data includes real-time temperature changes, and error temperature data includes error temperature changes. Real-time load data also includes the time of real-time load change. The real-time temperature change segment is extracted with the time of real-time load change as the center, and the temperature change in the real-time temperature change segment is the real-time temperature change. Error load data also includes the time of error load change. The error temperature change segment is extracted with the time of error load change as the center, and the temperature change in the error temperature change segment is the error temperature change. Based on real-time temperature changes and error temperature changes, the trend of workpiece error occurrence is predicted.

7. A workpiece parameter monitoring system for cycloidal spinning, characterized in that, include: The data acquisition module is used to collect the temperature data of the tool and the load data of the drive component in real time during the cycloidal spinning process. It is defined as real-time temperature data and real-time load data. The real-time temperature data includes the real-time temperature change interval, and the real-time load data includes the real-time load change interval. The real-time temperature data is collected by a temperature sensor, and the real-time load data is collected by a current sensor; the real-time temperature change interval is the interval between two consecutive temperature abrupt change points in real-time data collection, and the real-time load change interval is the interval between two consecutive load abrupt change points in real-time data collection. A real-time temperature waveform is generated based on temperature data collected by a temperature sensor, and a real-time load waveform is generated based on current data collected by a current sensor; the wavelength of the real-time temperature waveform is the real-time temperature change interval, and the wavelength of the real-time load waveform is the real-time load change interval. The data recording module is used to collect historical machining data when workpiece errors exist under various machining parameters. It extracts tool temperature data, drive load data, and corresponding workpiece errors from the historical machining data. Tool temperature data and drive load data are defined as error temperature data and error load data, respectively. Error temperature data includes the error temperature change interval, and error load data includes the error load change interval. The parameter analysis module is used to establish the relationship function between processing parameters, error temperature change interval, error load change interval and workpiece error, obtain real-time processing parameters and verify the real-time temperature change interval and real-time load change interval based on the relationship function, and judge the workpiece error.

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