Smart watch multi-key position CNC precision machining parameter dynamic optimization system

Abstract

This invention relates to the field of smartwatch button machining technology, specifically to a dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons. This system acquires relevant data in real time during the smartwatch button machining process; based on data from the roughing stage, it obtains the machining position and its stiffness level within the roughing stage, thereby identifying abnormal stiffness areas on the workpiece. It then adjusts the cutting feed rate for semi-finishing in these abnormal stiffness areas, obtaining the machining position and its stiffness level within the semi-finishing stage, and constructing a stiffness distribution map. Based on the distance between adjacent machining positions, vibration signal differences, and stiffness level differences in the stiffness distribution map, it obtains the stability level and subsequently the cutting feed rate for the machining position in the finishing stage. This invention, by predicting the cutting feed rate in real time during smartwatch button machining, avoids tool breakage and effectively improves the machining quality of smartwatch buttons.

Description

Technical Field

[0001] This invention relates to the field of smartwatch button processing technology, specifically to a dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons. Background Technology

[0002] As a core product in the wearable device field, smartwatches have maintained rapid market growth in recent years thanks to their diversified functions such as health monitoring, mobile communication, and mobile payment. With consumers' increasing demands for product experience, smartwatches are rapidly iterating towards higher integration, miniaturization, and precision. As a key component of human-computer interaction, the manufacturing quality of buttons directly determines the product's tactile feel, appearance, and durability, becoming one of the core factors affecting user experience.

[0003] Smartwatch buttons are characterized by their tiny size, complex structure, and stringent precision requirements. To ensure strength and wear resistance, high-strength, high-hardness materials such as stainless steel, titanium alloys, and ceramics are often used. To balance processing efficiency and precision, existing methods employ multi-station CNC (Computer Numerical Control) precision machining technology. This involves using multi-station fixtures to simultaneously clamp multiple blanks, sequentially completing the roughing, semi-finishing, and finishing processes to achieve mass production.

[0004] Existing multi-station CNC (Computer Numerical Control) precision machining methods collect machining process data in real time and dynamically adjust parameters based on preset rules (such as reducing the feed rate when the cutting force exceeds a threshold). However, these methods are still single-point, passive response optimizations that fail to fundamentally solve the problem. They remain reactive and cannot prevent problems before they occur. Furthermore, they lack the ability to correlate and analyze data from the entire machining process, making it impossible to predict subsequent machining risks based on previous machining information. Parameter adjustments are also lagging, which leads to inaccurate machining of smartwatch buttons. Summary of the Invention

[0005] To address the technical problem of inaccurate machining parameter settings during multi-station CNC (Computer Numerical Control) precision machining of smartwatch buttons, the present invention aims to provide a dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons. The specific technical solution adopted is as follows: This invention provides a dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons. The system includes: The data acquisition module is used to acquire in real time the electrical power signal, vibration signal, and cutting position coordinates of each cutting process during the manufacturing of smartwatch buttons; the manufacturing process includes roughing stage, semi-finishing stage, and finishing stage; The roughing stage analysis module is used to obtain the stiffness of each machining position in the roughing stage based on the electric power signal, vibration signal and cutting position coordinates of the cutting process; and to obtain the abnormal stiffness area of ​​the workpiece based on the stiffness in the roughing stage. The semi-finishing stage analysis module is used to adjust the cutting feed rate for semi-finishing in areas of abnormal stiffness of the workpiece according to the degree of stiffness, thereby obtaining the stiffness of each machining position in the semi-finishing stage. The stability acquisition module is used to construct a stiffness distribution map based on the processing location and its stiffness. According to the distance between each processing location and its adjacent processing locations, the difference in vibration signal, and the difference in stiffness in the stiffness distribution map, the stability of each processing location is obtained. The data processing module is used to obtain the cutting feed rate of each machining position during the finishing stage based on the stability level.

[0006] Furthermore, the method for obtaining the processing position is as follows: The cutting process is divided into time windows, and the average value of the cutting position coordinates within each time window is taken as the machining position corresponding to each time window.

[0007] Furthermore, the method for obtaining the stiffness level is as follows: For any machining position in any cutting process, the time window corresponding to that machining position is taken as the target time window; the electrical power signal in the target time window is converted into a cutting force signal, and the cutting force signal and vibration signal in the target time window are successively converted into cutting force frequency domain signal and vibration frequency domain signal through fast Fourier transform; Based on the relationship between the cutting force frequency domain signal and the vibration frequency domain signal, the stiffness function of the target time window is obtained; The frequency with the highest energy in the cutting force frequency domain signal is obtained as the main cutting frequency of the target time window; The stiffness corresponding to the main cutting frequency in the stiffness function is taken as the stiffness of the machining position.

[0008] Furthermore, the method for obtaining the stiffness function is as follows: The ratio of the complex number corresponding to the vibration frequency domain signal to the cutting force frequency domain signal is used as the frequency response function of the target time window; The reciprocal of the frequency response function is used as the stiffness function of the target time window.

[0009] Furthermore, the method for obtaining the abnormal stiffness region of the workpiece is as follows: The stiffness level during the roughing stage is identified by the 3σ anomaly identification method to obtain the abnormal stiffness level; The region where the processing position corresponding to the abnormal stiffness is located is taken as the abnormal stiffness region of the workpiece.

[0010] Furthermore, the method for adjusting the cutting feed rate for semi-finishing in the abnormal stiffness region of the workpiece according to the degree of stiffness is as follows: By analyzing the deviation direction between the abnormal stiffness level and the average stiffness level of all workpieces during the roughing stage, we can identify the abnormally high stiffness region and the abnormally low stiffness region of the workpiece. When semi-finishing is performed on an abnormally high stiffness region of the workpiece, the product of the preset standard cutting feed rate and the first preset weight is used as the first adjustment value. The result of adding the preset standard cutting feed rate to the first adjustment value is used as the cutting feed rate for semi-finishing the abnormally high stiffness region of the workpiece. When semi-finishing is performed on a workpiece in an area with abnormally low stiffness, the product of the preset standard cutting feed rate and the second preset weight is used as the second adjustment value. The difference between the preset standard cutting feed rate and the second adjustment value is used as the cutting feed rate for semi-finishing in areas of abnormally low stiffness in the workpiece.

[0011] Furthermore, the method for obtaining the stability level is as follows: For any machining position in the stiffness distribution diagram, the two adjacent machining positions before and after this machining position are both taken as analysis positions; For any analysis location, the result of negatively correlated and normalized distance between the processing location and the analysis location is used as a reference weight; The first difference is obtained based on the difference in vibration signals between the processing position and the analysis position; The difference in stiffness between the machining location and the analysis location is taken as the second difference; Based on the magnitude of the first and second differences, and the magnitude of the reference weights, the similarity between the processing location and the analysis location is obtained; The result of normalizing the mean similarity between the processing location and each analysis location is taken as the stability of the processing location.

[0012] Furthermore, the method for obtaining the first difference is as follows: The preset number of frequencies with the highest energy in the vibration frequency domain signal within the time window corresponding to the processing position are all taken as the first frequency; The preset number of frequencies with the highest energy in the vibration frequency domain signal within the time window corresponding to the analysis location are all taken as the second frequency; Arrange the first frequencies in descending order of their corresponding energies to obtain the first frequency sequence; Arrange the second frequencies in descending order of their corresponding energies to obtain the second frequency sequence; The first difference is the result of normalizing the sum of the differences between the elements at the same position in the first frequency sequence and the second frequency sequence.

[0013] Furthermore, the method for obtaining the degree of similarity is as follows: The sum of the first and second differences is taken as the overall difference; The product of the negative correlation of the overall difference and the reference weight is used as the similarity between the processing location and the analysis location.

[0014] Furthermore, the method for obtaining the cutting feed rate at each machining position during the finishing stage based on stability is as follows: The difference between the maximum and minimum feed rates within the preset feed rate range is used as the overall adjustment value. For any machining position, the product of the stability of that machining position and the overall adjustment value is used as the cutting feed rate adjustment value for that machining position; The minimum cutting feed rate within the preset cutting feed rate range is added to the cutting feed rate adjustment value, and the result is taken as the cutting feed rate for that machining position in the finishing stage.

[0015] The present invention has the following beneficial effects: This invention first obtains the stiffness level of each machining position during the roughing stage based on the electrical power signal, vibration signal, and cutting position coordinates during the cutting process. This accurately reflects the machining status of the smartwatch button during the roughing stage and determines the stiffness distribution of each position of the smartwatch button, preparing for accurate subsequent machining. Then, based on the stiffness level during the roughing stage, abnormal stiffness areas of the workpiece are identified, allowing for accurate adjustment of the cutting feed rate in the subsequent semi-finishing stage, resulting in more precise machining of the smartwatch button. Finally, the cutting feed rate for semi-finishing is adjusted according to the magnitude of the stiffness level in the abnormal stiffness areas of the workpiece, further ensuring more accurate acquisition of the machining status of each machining position during the semi-finishing stage. The stiffness of the machining position ensures better stability and quality during the finishing stage. To accurately and stably machine the smartwatch buttons during the finishing stage and improve machining efficiency, a stiffness distribution map is constructed based on the machining position and its stiffness. The stability of each machining position is obtained by considering its distance from adjacent positions, vibration signal differences, and stiffness differences. This accurately reflects the material uniformity and machining stability at each position. Based on this stability, the feed rate for each machining position during the finishing stage is determined, effectively improving machining efficiency while preventing tool breakage and enhancing the machining quality and accuracy of the smartwatch buttons. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a structural block diagram of a dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a computer device provided according to an embodiment of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the dynamic optimization system for multi-station CNC precision machining of smartwatch buttons proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons provided by this invention.

[0021] Example 1: This invention proposes a dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons. Please refer to [link / reference]. Figure 1 The diagram shows a structural block diagram of a dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons provided in an embodiment of the present invention. The system includes: a data acquisition module 10, a roughing stage analysis module 20, a semi-finishing stage analysis module 30, a stability acquisition module 40, and a data processing module 50.

[0022] The data acquisition module 10 is used to acquire in real time the electrical power signal, vibration signal and cutting position coordinates of each cutting process during the smartwatch button processing; the processing process includes roughing stage, semi-finishing stage and finishing stage.

[0023] Specifically, due to the small size of smartwatch buttons, a multi-station precision fixture is typically used to clamp multiple workpieces simultaneously. This involves loading the metal blanks for the smartwatch buttons (such as small pieces cut from stainless steel bars) one by one into the various clamping slots of the multi-station precision fixture, and then activating the clamping device to ensure the metal blanks are properly held in place. For better description, this embodiment uses the processing of a single smartwatch button as an example; all subsequent references to "smartwatch button" will refer to this specific smartwatch button.

[0024] To accurately analyze the manufacturing process of smartwatch buttons, this embodiment acquires the electrical power signal, vibration signal, and cutting position coordinates for each cutting process in real time. The electrical power signal is acquired by a power sensor on the cutting tool, the vibration signal is acquired by a vibration sensor on the metal blank of the workpiece (the smartwatch button), and the cutting position coordinates are the position of the tool on the workpiece. This embodiment assumes the workpiece is in a three-dimensional coordinate system; however, the implementer can set the position of the workpiece in the three-dimensional coordinate system according to actual conditions, which is not limited here. The electrical power signal, vibration signal, and cutting position coordinates for each cutting process are acquired synchronously. This embodiment sets the frequency of synchronous data acquisition for each cutting process to 50Hz; however, the implementer can set the frequency of data acquisition according to actual conditions, which is not limited here. It should be noted that this embodiment first initializes a global data table, which is synchronized with the G-code line number of CNC precision machining, ensuring that each cutting process is allocated a unique data storage area, thus ensuring accurate analysis of each cutting process. CNC precision machining is well-known and will not be described in detail here.

[0025] Furthermore, the manufacturing process of smartwatch buttons includes roughing, semi-finishing, and finishing stages. In the roughing stage, a slightly larger diameter tool is used with a high feed rate and deep cut to quickly remove most of the excess material from the metal blank, leaving some allowance for subsequent machining. The semi-finishing stage prepares for finishing by further removing excess material from the metal blank and making it more uniform, ensuring the stability and quality of the finishing process. The finishing stage uses a small-diameter, multi-edged precision tool with a high rotation speed, shallow depth of cut, and low feed rate to achieve the final geometry and surface quality of the smartwatch buttons.

[0026] The roughing stage analysis module 20 is used to obtain the stiffness of each machining position in the roughing stage based on the electric power signal, vibration signal and cutting position coordinates of the cutting process in the roughing stage; and to obtain the abnormal stiffness area of ​​the workpiece based on the stiffness in the roughing stage.

[0027] Given that the material distribution of the metal blank is not uniform, this embodiment first analyzes the roughing stage to better capture the smartwatch buttons. During the roughing stage, the metal blank is cut according to initially set parameters (e.g., the initial feed rate and initial electrical power of the cutting tool). Then, based on the electrical power signal, vibration signal, and cutting position coordinates during the roughing stage, the stiffness of each machining position is obtained. This preliminary analysis of the stiffness of various parts of the metal blank reflects the material distribution in those parts, which is beneficial for adjusting the cutting feed rate in the subsequent semi-finishing stage. This improves the machining quality of the metal blank while avoiding tool breakage. Furthermore, this embodiment identifies abnormal stiffness areas in the workpiece based on the stiffness level during the roughing stage, providing accurate data for subsequent adjustments to the cutting feed rate.

[0028] Preferably, in one feasible method of this embodiment, the processing position is obtained by dividing the cutting process into time windows. In this embodiment, the duration of the time window is set to 0.1 seconds. The implementer can set the duration of the time window according to the actual situation, and it is not limited here. It should be noted that if the duration of the last time segment of a certain cutting process is less than the duration of a time window, it is still considered as a time window by default. In order to accurately and efficiently analyze the stiffness of the metal blank, the average value of the cutting position coordinates within each time window is taken as the processing position corresponding to each time window.

[0029] This completes the acquisition of each processing position within the roughing stage.

[0030] Preferably, in one feasible embodiment of this invention, the method for obtaining the stiffness is as follows: for any machining position in any cutting process, the time window corresponding to that machining position is taken as the target time window; the electrical power signal in the target time window is converted into a cutting force signal, wherein the method for converting the electrical power signal into a cutting force signal is well-known and will not be described in detail. Further, the cutting force signal and vibration signal in the target time window are sequentially converted into a cutting force frequency domain signal and a vibration frequency domain signal using a Fast Fourier Transform (FFT); wherein the FFT is a well-known technique and will not be described in detail. The cutting force frequency domain signal is essentially an input excitation signal, representing the intensity distribution of the cutting force at different frequencies; the vibration frequency domain signal is essentially an output response signal, representing the intensity distribution of the vibration of the metal blank, i.e., the machined workpiece, at different frequencies; simultaneously, both the cutting force frequency domain signal and the vibration frequency domain signal exist in complex form, containing two types of information: amplitude (corresponding to the intensity of the signal at a certain frequency) and phase (corresponding to the time delay characteristics of the signal). In the manufacturing of smartwatch buttons, the cutting force is a small-amplitude excitation (the workpiece size is small, the machining precision is high, and the cutting amount is controllable). The dynamic response of the machined part under this excitation can be approximated as a linear system (nonlinear effects are negligible). For a linear system, if the input excitation is a sinusoidal signal of a certain frequency, the output response must also be a sinusoidal signal of the same frequency (only the amplitude and phase may change). Therefore, the ratio of the output response to the input excitation at each frequency point in the frequency domain can uniquely describe the dynamic characteristics of the system at that frequency. The larger the ratio of the output response to the input excitation, the stronger the vibration response generated under the same excitation (cutting force), which indirectly indicates that the stiffness of the machining position is lower. Furthermore, this embodiment obtains the stiffness function of the target time window based on the relationship between the cutting force frequency domain signal and the vibration frequency domain signal. The stiffness function is obtained by taking the ratio of the complex numbers corresponding to the vibration frequency domain signal and the cutting force frequency domain signal as the frequency response function of the target time window. Since both the vibration frequency domain signal and the cutting force frequency domain signal are complex numbers, the frequency response function also exists in complex form. The amplitude of the frequency response function reflects the vibration intensity of the system under unit cutting force excitation; the larger the amplitude, the more sensitive the system is to that frequency excitation. The phase of the frequency response function reflects the time lag of the vibration response relative to the cutting force excitation (related to the system's damping characteristics). Therefore, the frequency response function essentially reflects the ability of the machined part to generate a vibration response under a cutting force at a certain frequency. Its value is directly related to stiffness; the stronger the vibration response under the same cutting force excitation, the larger the value of the frequency response function, and the smaller the stiffness. Thus, the stiffness and the frequency response function are inversely related. Therefore, the reciprocal of the frequency response function is used as the stiffness function of the target time window. It should be noted that, since the frequency response function is a complex number, the stiffness function is also a complex number. The amplitude of the stiffness function directly corresponds to the stiffness of the workpiece at a certain frequency (the larger the amplitude, the greater the stiffness). The phase of the stiffness function helps to reflect the dynamic change characteristics of the stiffness. Together, they constitute complete stiffness information. It should be noted that this embodiment does not consider the case where the cutting force frequency domain signal and the frequency response function are 0. The stiffness function obtained above is a continuous distribution relationship between frequency and stiffness in the target time window. In this embodiment, the frequency with the highest energy in the cutting force frequency domain signal is obtained as the main cutting frequency in the target time window. Then, the stiffness corresponding to the main cutting frequency in the stiffness function is taken as the stiffness of the machining position.

[0031] At this point, the stiffness of each machining position during the rough machining stage is obtained.

[0032] Preferably, in one feasible embodiment of this invention, the method for obtaining the abnormal stiffness region of the workpiece is as follows: The abnormal stiffness level is identified by using the 3σ anomaly identification method during the rough machining stage; wherein, the 3σ anomaly identification method is a known technology and will not be described in detail. Then, the region where the machining position corresponding to the abnormal stiffness level is located is taken as the abnormal stiffness region of the workpiece. It should be noted that the abnormal stiffness region of the workpiece is generally divided into a high-stiffness region and a low-stiffness region, which will be described in detail later. Essentially, the abnormal stiffness region of the workpiece is the region formed by connecting the machining positions corresponding to the abnormal stiffness level; in special cases, it can be a line segment or a point.

[0033] The semi-finishing stage analysis module 30 is used to adjust the cutting feed rate of the abnormal stiffness area of ​​the workpiece for semi-finishing according to the magnitude of the stiffness, thereby obtaining the stiffness of each machining position in the semi-finishing stage.

[0034] Specifically, during the roughing stage, abnormal stiffness regions of the workpiece were identified. To achieve higher quality machining of the metal blank and ultimately better quality smartwatch buttons, this embodiment adjusts the cutting feed rate for semi-finishing the abnormal stiffness regions based on the degree of stiffness. This allows for better semi-finishing of the metal blank, facilitating more accurate finishing processing in the subsequent stages. Based on the adjusted cutting feed rate, the stiffness of each machining position within the semi-finishing stage is obtained, preparing for subsequent adjustments to the cutting feed rate during the finishing stage.

[0035] Preferably, in one feasible embodiment of this invention, the method for adjusting the cutting feed rate for semi-finishing in the abnormal stiffness region of the workpiece according to the degree of stiffness is as follows: By analyzing the deviation direction of the abnormal stiffness level from the average stiffness level of all stiffness levels during the roughing stage, the abnormally high stiffness region and the abnormally low stiffness region of the workpiece are obtained. Among them, the abnormal stiffness region of the workpiece corresponding to the machining position with an abnormal stiffness level less than the average stiffness level of all stiffness levels during the roughing stage is designated as the abnormally low stiffness region of the workpiece; the abnormal stiffness region of the workpiece corresponding to the machining position with an abnormal stiffness level greater than the average stiffness level of all stiffness levels during the roughing stage is designated as the abnormally high stiffness region of the workpiece. The characteristic of abnormally low stiffness regions on a workpiece is that the material has a weak ability to resist cutting deformation; that is, under the same cutting force, this region is more prone to elastic or plastic deformation. One of the goals of semi-finishing is to provide uniform allowance and a stable surface condition for finishing. If a high feed rate is used in abnormally low stiffness regions of the workpiece, it will cause significant deformation of the metal blank (such as local depressions or bending), leading to out-of-tolerance surface dimensional accuracy after semi-finishing, which is difficult to correct in subsequent finishing. At the same time, it will also lead to amplified vibration and deterioration of surface quality, i.e., the appearance of vibration marks on the machined surface, directly affecting the appearance and feel of smartwatch buttons. Therefore, the cutting feed rate in abnormally low stiffness regions of the workpiece should be reduced to reduce the risk of deformation and vibration, ensuring the surface accuracy and uniformity of allowance after semi-finishing, laying the foundation for subsequent finishing. The characteristic of abnormally high stiffness regions on a workpiece is that the material has a strong ability to resist cutting deformation; that is, under the same cutting force, the deformation in this region is minimal, and the machining stability is high. In this case, machining with ordinary parameters is too conservative and will significantly prolong the machining time. Therefore, the cutting feed rate in the abnormally high stiffness region of the workpiece should be increased, which can increase the amount of material removed per unit time and shorten the processing cycle without sacrificing quality; Therefore, when semi-finishing is performed on an abnormally high stiffness region of the workpiece, the product of the preset standard cutting feed rate and the first preset weight is used as the first adjustment value; the sum of the preset standard cutting feed rate and the first adjustment value is used as the cutting feed rate for semi-finishing the abnormally high stiffness region of the workpiece. When semi-finishing is performed on an abnormally low stiffness region of the workpiece, the product of the preset standard cutting feed rate and the second preset weight is used as the second adjustment value; the difference between the preset standard cutting feed rate and the second adjustment value is used as the cutting feed rate for semi-finishing the abnormally low stiffness region of the workpiece. In this embodiment, the first preset weight is set to 0.3. The implementer can set the size of the first preset weight and the first preset weight according to the actual situation, which is not limited here. The preset standard cutting feed rate is essentially the standard cutting feed rate initially set by a professional, and is not limited here.

[0036] It should be noted that for semi-finishing in areas where the workpiece does not exhibit abnormal stiffness, machining parameters obtained through table lookup are still used. The table lookup method is well-known and will not be elaborated upon further.

[0037] The stability acquisition module 40 is used to construct a stiffness distribution map based on the processing position and its stiffness. Based on the distance between each processing position and its adjacent processing positions, the difference in vibration signal, and the difference in stiffness in the stiffness distribution map, the stability of each processing position is obtained.

[0038] Specifically, after the roughing and semi-finishing stages, the smartwatch buttons are almost fully formed. The finishing stage requires further fine-tuning of the workpiece. Therefore, this embodiment combines the machining positions and stiffness levels from the roughing and semi-finishing stages to obtain a stiffness distribution map. It should be noted that the stiffness distribution map corresponds to the workpiece after the semi-finishing stage. In the stiffness distribution map, when a machining position is closer to its adjacent positions, the vibration signal and stiffness are more similar between the machining position and its adjacent positions, indicating a more uniform material and stiffness at that machining position. In the finishing stage, the feed rate at this machining position can be increased to improve the machining efficiency of the smartwatch buttons. Furthermore, this embodiment obtains the stability of each machining position based on the distance between each machining position and its adjacent positions, the difference in vibration signal, and the difference in stiffness level in the stiffness distribution map. The greater the stability, the greater the feed rate should be at the corresponding machining position in the finishing stage.

[0039] Preferably, in one feasible embodiment of this invention, the method for obtaining the stability level is as follows: for any processing position in the stiffness distribution map, the two adjacent processing positions before and after this processing position are both taken as analysis positions; for any analysis position, the result of negatively correlated and normalized Euclidean distance between the processing position and the analysis position is used as a reference weight; the larger the reference weight, the shorter the Euclidean distance between the processing position and the analysis position, and the more meaningful the analysis position is for the stability analysis of the processing position. In this embodiment, the negative of the above Euclidean distance is used as the power of an exponential function with the natural constant as the base, and the output of the exponential function is the result of negatively correlated and normalized Euclidean distance. The method for obtaining the Euclidean distance is a well-known technique and will not be described in detail here. Further, based on the difference in vibration signals between the processing location and the analysis location, a first difference is obtained. The smaller the first difference, the more similar the vibration signals of the processing location and the analysis location are, indirectly indicating that the material distribution at the processing location is more uniform. The method for obtaining the first difference is as follows: a preset number of frequencies with the highest energy in the vibration frequency domain signal within the time window corresponding to the processing location are all taken as the first frequency; a preset number of frequencies with the highest energy in the vibration frequency domain signal within the time window corresponding to the analysis location are all taken as the second frequency. In this embodiment, the preset number is set to 3, but the implementer can set the size of the preset number according to the actual situation, which is not limited here. It should be noted that if the number of frequencies in the vibration frequency domain signal is less than the preset number, it is padded with 0. Then, the first frequencies are arranged in descending order of their corresponding energy to obtain a first frequency sequence; the second frequencies are arranged in descending order of their corresponding energy to obtain a second frequency sequence. To accurately characterize the difference in vibration signals between the processing location and the analysis location, the absolute values ​​of the differences between the elements at the same position in the first frequency sequence and the second frequency sequence are added together and normalized, and the result is taken as the first difference. This embodiment normalizes the sum of the absolute values ​​of the above differences using the minimum-maximum normalization method. The dataset corresponding to the minimum-maximum normalization method is the sum of the absolute values ​​of the differences corresponding to all processing positions. The minimum-maximum normalization method is well-known and will not be elaborated further. In order to more accurately analyze the difference between the machining position and the analysis position, the absolute value of the difference in stiffness between the machining position and the analysis position is taken as the second difference; When both the first and second differences are smaller, and the reference weight is larger, it indicates that the material and stiffness of the processing location are more similar to those of the analysis location, indirectly indicating that the material and stiffness of the processing location are more stable. Therefore, this embodiment obtains the similarity between the processing location and the analysis location based on the magnitude of the first and second differences and the magnitude of the reference weight. The method for obtaining the similarity is as follows: the sum of the first and second differences is taken as the overall difference; then, the product of the negative correlation result of the overall difference and the reference weight is taken as the similarity between the processing location and the analysis location. This embodiment uses the negative of the overall difference as the power of an exponential function with the natural constant as its base, and the output of this exponential function is the negative correlation result of the overall difference. This allows the similarity between the processing location and each analysis location to be obtained. When the similarity is larger, it indicates that the material and stiffness of the processing location are more uniform; therefore, the result of normalizing the mean of the similarity between the processing location and each analysis location is taken as the stability of the processing location. In this embodiment, the mean of the above similarity is normalized by the minimum-maximum normalization method. The dataset corresponding to the minimum-maximum normalization method is the mean of the similarity of all processing positions in the entire stiffness distribution map.

[0040] At this point, the stability of each processing location in the stiffness distribution diagram is obtained.

[0041] The data processing module 50 is used to obtain the cutting feed rate of each machining position during the finishing stage based on the stability.

[0042] It is known that the greater the stability, the more uniform the material and the more stable the processing state at the corresponding machining position. To improve the processing efficiency of smartwatch buttons, the feed rate at the corresponding machining position should be increased during the finishing stage. Therefore, this embodiment obtains the feed rate of each machining position during the finishing stage based on the stability. The specific method is as follows: the difference between the maximum feed rate and the minimum feed rate within a preset feed rate range is used as the overall adjustment value; the preset feed rate range is set by professionals and is not limited here. For any machining position, the product of the stability of the machining position and the overall adjustment value is used as the feed rate adjustment value for that machining position; the sum of the minimum feed rate within the preset feed rate range and the feed rate adjustment value is used as the feed rate for that machining position during the finishing stage.

[0043] At this point, the cutting feed rate for each machining position during the finishing stage is obtained. It should be noted that the finishing stage involves performing the final machining process on the workpiece based on the machining positions shown in the stiffness distribution diagram.

[0044] In summary, this embodiment acquires relevant data in real time during the smartwatch button machining process. Based on the data from the roughing stage, it obtains the machining position and its stiffness level within the roughing stage, thereby identifying abnormal stiffness regions of the workpiece. The feed rate for semi-finishing is adjusted to address these abnormal stiffness regions, and the machining position and its stiffness level within the semi-finishing stage are then obtained. A stiffness distribution map is constructed, and based on the distance between adjacent machining positions, vibration signal differences, and stiffness level differences in the stiffness distribution map, the stability level is determined, leading to the acquisition of the feed rate for the machining position in the finishing stage. This invention, by predicting the feed rate in real time during the smartwatch button machining process, avoids tool breakage and effectively improves the machining quality of the smartwatch buttons.

[0045] Example 2: This invention also proposes a device for dynamic optimization of CNC precision machining parameters for smartwatch buttons at multiple workstations. This device includes a memory and a processor. The memory stores executable program code, and the processor calls and executes this executable program code to perform the dynamic optimization system for CNC precision machining parameters for smartwatch buttons provided in this embodiment. Specifically, the device can be a chip, component, or module. The chip may include a connected processor and memory; the memory stores instructions, and when the processor calls and executes the instructions, the chip can perform the dynamic optimization system for CNC precision machining parameters for smartwatch buttons provided in the above embodiment.

[0046] Furthermore, this application also protects a computer device; please refer to [link to relevant documentation]. Figure 2 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute the aforementioned dynamic optimization system for multi-station CNC precision machining parameters of any smartwatch button.

[0047] Example 3: The present invention also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to realize the dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons provided in the above embodiments.

[0048] Example 4: The present invention also provides a computer program product, which, when run on a computer, causes the computer to perform the above-mentioned related steps to realize the dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons provided in the above embodiments.

[0049] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0050] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons, characterized in that, The system includes: The data acquisition module is used to acquire in real time the electrical power signal, vibration signal, and cutting position coordinates of each cutting process during the manufacturing of smartwatch buttons; the manufacturing process includes roughing stage, semi-finishing stage, and finishing stage; The roughing stage analysis module is used to obtain the stiffness of each machining position in the roughing stage based on the electric power signal, vibration signal and cutting position coordinates of the cutting process; and to obtain the abnormal stiffness area of ​​the workpiece based on the stiffness in the roughing stage. The semi-finishing stage analysis module is used to adjust the cutting feed rate for semi-finishing in areas of abnormal stiffness of the workpiece according to the degree of stiffness, thereby obtaining the stiffness of each machining position in the semi-finishing stage. The stability acquisition module is used to construct a stiffness distribution map based on the processing location and its stiffness. According to the distance between each processing location and its adjacent processing locations, the difference in vibration signal, and the difference in stiffness in the stiffness distribution map, the stability of each processing location is obtained. The data processing module is used to obtain the cutting feed rate of each machining position during the finishing stage based on the stability level.

2. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 1, characterized in that, The method for obtaining the processing position is as follows: The cutting process is divided into time windows, and the average value of the cutting position coordinates within each time window is taken as the machining position corresponding to each time window.

3. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 2, characterized in that, The method for obtaining the stiffness level is as follows: For any machining position in any cutting process, the time window corresponding to that machining position is taken as the target time window; the electrical power signal in the target time window is converted into a cutting force signal, and the cutting force signal and vibration signal in the target time window are successively converted into cutting force frequency domain signal and vibration frequency domain signal through fast Fourier transform; Based on the relationship between the cutting force frequency domain signal and the vibration frequency domain signal, the stiffness function of the target time window is obtained; The frequency with the highest energy in the cutting force frequency domain signal is obtained as the main cutting frequency of the target time window; The stiffness corresponding to the main cutting frequency in the stiffness function is taken as the stiffness of the machining position.

4. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 3, characterized in that, The method for obtaining the stiffness function is as follows: The ratio of the complex number corresponding to the vibration frequency domain signal to the cutting force frequency domain signal is used as the frequency response function of the target time window; The reciprocal of the frequency response function is used as the stiffness function of the target time window.

5. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 1, characterized in that, The method for obtaining the abnormal stiffness region of the workpiece is as follows: The stiffness level during the roughing stage is identified by the 3σ anomaly identification method to obtain the abnormal stiffness level; The region where the processing position corresponding to the abnormal stiffness is located is taken as the abnormal stiffness region of the workpiece.

6. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 5, characterized in that, The method for adjusting the cutting feed rate for semi-finishing in areas of abnormal workpiece stiffness based on the degree of stiffness is as follows: By analyzing the deviation direction between the abnormal stiffness level and the average stiffness level of all workpieces during the roughing stage, we can identify the abnormally high stiffness region and the abnormally low stiffness region of the workpiece. When semi-finishing is performed on an abnormally high stiffness region of the workpiece, the product of the preset standard cutting feed rate and the first preset weight is used as the first adjustment value. The result of adding the preset standard cutting feed rate to the first adjustment value is used as the cutting feed rate for semi-finishing the abnormally high stiffness region of the workpiece. When semi-finishing is performed on a workpiece in an area with abnormally low stiffness, the product of the preset standard cutting feed rate and the second preset weight is used as the second adjustment value. The difference between the preset standard cutting feed rate and the second adjustment value is used as the cutting feed rate for semi-finishing in areas of abnormally low stiffness in the workpiece.

7. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 3, characterized in that, The method for obtaining the stability level is as follows: For any machining position in the stiffness distribution diagram, the two adjacent machining positions before and after this machining position are both taken as analysis positions; For any analysis location, the result of negatively correlated and normalized distance between the processing location and the analysis location is used as a reference weight; The first difference is obtained based on the difference in vibration signals between the processing position and the analysis position; The difference in stiffness between the machining location and the analysis location is taken as the second difference; Based on the magnitude of the first and second differences, and the magnitude of the reference weights, the similarity between the processing location and the analysis location is obtained; The result of normalizing the mean similarity between the processing location and each analysis location is taken as the stability of the processing location.

8. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 7, characterized in that, The method for obtaining the first difference is as follows: The preset number of frequencies with the highest energy in the vibration frequency domain signal within the time window corresponding to the processing position are all taken as the first frequency; The preset number of frequencies with the highest energy in the vibration frequency domain signal within the time window corresponding to the analysis location are all taken as the second frequency; Arrange the first frequencies in descending order of their corresponding energies to obtain the first frequency sequence; Arrange the second frequencies in descending order of their corresponding energies to obtain the second frequency sequence; The first difference is the result of normalizing the sum of the differences between the elements at the same position in the first frequency sequence and the second frequency sequence.

9. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 7, characterized in that, The method for obtaining the similarity is as follows: The sum of the first and second differences is taken as the overall difference; The product of the negative correlation of the overall difference and the reference weight is used as the similarity between the processing location and the analysis location.

10. The dynamic optimization system for multi-station CNC precision machining parameters of smartwatch buttons as described in claim 1, characterized in that, The method for obtaining the cutting feed rate of each machining position during the finishing stage based on stability is as follows: The difference between the maximum and minimum feed rates within the preset feed rate range is used as the overall adjustment value. For any machining position, the product of the stability of that machining position and the overall adjustment value is used as the cutting feed rate adjustment value for that machining position; The minimum cutting feed rate within the preset cutting feed rate range is added to the cutting feed rate adjustment value, and the result is taken as the cutting feed rate for that machining position in the finishing stage.

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