A workpiece machining optimization method, device, equipment and medium

Abstract

The application provides a workpiece machining optimization method, device, equipment and medium, which are used in the technical field of workpiece machining and can solve the machining abnormal problem caused by vibration. The method comprises the following steps: determining the frequency response function of the workpiece and the tool, and determining the weak stiffness component from the workpiece and the tool according to the amplitude of the frequency response function; collecting at least one vibration parameter of the weak stiffness component to obtain a vibration signal, and judging whether the weak stiffness component vibrates according to the signal characteristic value of the vibration signal and the frequency domain peak value of the corresponding frequency domain signal; if yes, determining a dynamic model according to the frequency response characteristic value of the weak stiffness component and performing stability calculation to obtain a stability characteristic map; determining a target cutting parameter according to the characteristic curve in the stability characteristic map, and adjusting the process parameter of the weak stiffness component according to the target cutting parameter; and machining the weak stiffness component according to the adjusted process parameter. In this way, the accuracy and efficiency of workpiece machining are improved.

Description

Technical Field

[0001] This application relates to the field of workpiece processing technology, and in particular to a workpiece processing optimization method, apparatus, equipment and medium. Background Technology

[0002] Aircraft thin-walled parts refer to parts in the aircraft structure with a very small ratio of wall thickness to their own outline dimensions. The wall thickness is usually only a few millimeters or even less. Aircraft thin-walled parts are widely used in the aerospace field because of their light weight, compact structure and excellent performance. During the processing of such parts, the problem of tool ejection often occurs due to unreasonable selection of process parameters, which seriously affects the processing quality and efficiency of the parts.

[0003] Existing machining optimization methods mainly avoid resonance by reducing the spindle speed, forcing the cutting excitation frequency to be out of sync with the natural frequency of the part.

[0004] However, existing machining optimization methods generally have limited effectiveness in addressing vibration issues during machining. Summary of the Invention

[0005] This application provides a workpiece machining optimization method, apparatus, equipment, and medium to solve the problem that existing machining optimization methods generally have limited effectiveness in dealing with vibration issues during machining.

[0006] In a first aspect, this application provides a method for optimizing the processing of a workpiece, the method comprising: Based on the vibration response of the workpiece and the tool under the preset hammering operation, the corresponding frequency response function is determined, and based on the amplitude of the frequency response function, the weak rigid components are identified from the workpiece and the tool. At least one vibration parameter of a weakly rigid component is collected during real-time processing to obtain a vibration signal. Based on the signal characteristic value of the vibration signal and the frequency peak value of the corresponding frequency domain signal, it is determined whether the weakly rigid component is vibrating. If so, then based on the frequency response characteristic value of the weakly rigid component, determine the dynamic model, and perform stability calculations on the dynamic model to obtain a stability characteristic diagram; the frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component. Based on the characteristic curves in the stable feature map, the target cutting parameters are determined, and the process parameters of the weakly rigid component are adjusted according to the target cutting parameters. The weakly rigid components are processed according to the adjusted process parameters.

[0007] In some embodiments of this application, based on the vibration response of the workpiece and the cutting tool under a preset hammering operation, a corresponding frequency response function is determined, and based on the amplitude of the frequency response function, a weakly rigid component is identified from the workpiece and the cutting tool, including: Determine the excitation signal corresponding to the excitation source of the preset hammering operation, and the response signal corresponding to the vibration response; Perform a Fourier transform on the excitation signal to obtain the excitation frequency domain signal, and perform a Fourier transform on the response signal to obtain the response frequency domain signal; The frequency response function is determined by the ratio of the response frequency domain signal to the excitation frequency domain signal.

[0008] In some embodiments of this application, the signal characteristic values ​​include the signal variance; The system collects at least one vibration parameter of a weakly rigid component under real-time machining operations to obtain a vibration signal. Based on the signal characteristic values ​​of the vibration signal and the corresponding frequency domain peak value, it determines whether the weakly rigid component is experiencing vibration flutter, including: Calculate the signal variance corresponding to the vibration signal; The frequencies corresponding to each amplitude of the frequency response function are determined as the natural frequencies of the weakly rigid component, resulting in multiple natural frequencies. The peak frequencies corresponding to each frequency domain peak in the frequency domain signal are then determined. The comparison results are obtained by comparing the signal variance with the preset variance, as well as the peak frequency with the natural frequency. Based on the comparison results, determine whether the weakly rigid component is experiencing vibration.

[0009] In some embodiments of this application, determining whether a weakly rigid component vibrates based on the comparison results includes: If the comparison result shows that the signal variance is greater than the preset variance and there is a peak frequency in the peak frequency that is the same as any natural frequency, then it is determined that the weak rigid component is vibrating. If the comparison result is that the signal variance is not greater than the preset variance, or there is no peak frequency in the peak frequency that is the same as any natural frequency, then it is determined that the weak rigid component has not vibrated.

[0010] In some embodiments of this application, a dynamic model is determined based on the frequency response characteristic values ​​of the weakly rigid component, and a stability calculation is performed on the dynamic model to obtain a stability characteristic map, including: Determine multiple amplitude values ​​of the frequency response function corresponding to the weakly rigid component; Based on the frequencies corresponding to each amplitude, determine multiple natural frequencies in the frequency response characteristic value; The half-power bandwidth of each amplitude is calculated to obtain multiple damping coefficients in the frequency response characteristic value; Based on the amplitude and the mass of the weakly rigid component, determine multiple stiffness values ​​in the frequency response characteristic values; Substituting the natural frequencies, damping coefficients, and stiffness into the vibration rules yields the dynamic model.

[0011] In some embodiments of this application, vibration parameters include cutting speed; Based on the characteristic curves in the stable feature map, the target cutting parameters are determined, and the process parameters of the weakly rigid component are adjusted according to the target cutting parameters, including: Determine the horizontal and vertical axes of the stable feature map; the horizontal axis is used to characterize the cutting speed corresponding to the weak rigid component, and the vertical axis is used to characterize the cutting depth corresponding to the weak rigid component. Based on the corresponding horizontal and vertical coordinates of the characteristic curve, the target cutting depth corresponding to each cutting speed of the weak rigid component is determined, and the corresponding real-time cutting depth is determined from the target cutting depth based on the real-time cutting speed of the weak rigid component under real-time machining operation. Based on the real-time cutting speed and real-time cutting depth, the target cutting parameters are determined, and the process parameters of the weakly rigid parts are adjusted according to the target cutting parameters.

[0012] In some embodiments of this application, after determining the target cutting parameters based on the feature curves in the stable feature map, and adjusting the process parameters of the weakly rigid component based on the target cutting parameters, the method further includes: Strengthen the stiffness of weakly rigid components.

[0013] Secondly, this application provides a workpiece processing optimization apparatus, the apparatus comprising: The determination module is used to determine the corresponding frequency response function based on the vibration response of the workpiece and the tool under a preset hammering operation, and to determine the weak rigid component from the workpiece and the tool based on the amplitude of the frequency response function. The judgment module is used to collect at least one vibration parameter of the weak rigid component under real-time processing operation, obtain the vibration signal, and determine whether the weak rigid component is vibrating based on the signal characteristic value of the vibration signal and the frequency domain peak value of the corresponding frequency domain signal. The calculation module is used to determine the dynamic model based on the frequency response characteristic value of the weakly rigid component if the condition is met, and to perform stability calculation on the dynamic model to obtain a stability characteristic map; the frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component. The adjustment module is used to determine the target cutting parameters based on the characteristic curves in the stable feature map, and to adjust the process parameters of the weakly rigid parts according to the target cutting parameters. The machining module is used to machine weakly rigid components according to the adjusted process parameters.

[0014] Thirdly, this application provides an apparatus, including: a processor, and a memory communicatively connected to the processor; The memory stores the instructions that the computer executes; The processor executes computer execution instructions stored in memory to implement the method of this application.

[0015] Fourthly, this application provides a computer-readable storage medium storing program code, which, when executed by a processor, is used to implement the method of this application.

[0016] This application provides a workpiece machining optimization method, apparatus, equipment, and medium. The method involves determining the corresponding frequency response function based on the vibration response of the workpiece and tool under a preset hammering operation, and identifying weakly rigid components from the workpiece and tool based on the amplitude of the frequency response function. At least one vibration parameter of the weakly rigid component is collected during real-time machining operations to obtain a vibration signal. Based on the signal characteristic value of the vibration signal and the frequency domain peak value of the corresponding frequency domain signal, it is determined whether the weakly rigid component is vibrating. If so, a dynamic model is determined based on the frequency response characteristic value of the weakly rigid component, and stability calculations are performed on the dynamic model to obtain a stability characteristic diagram. The frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component. Target cutting parameters are determined based on the characteristic curve in the stability characteristic diagram, and the process parameters of the weakly rigid component are adjusted according to the target cutting parameters. The weakly rigid component is then machined according to the adjusted process parameters.

[0017] In this way, by performing vibration analysis on the workpiece and the cutting tool, the frequency response function of the cutting tool and the workpiece can be obtained. Based on the amplitude of the frequency response function, the weak rigid parts can be identified, so as to further collect and analyze the machining process signals. Combined with dynamic theory analysis, when chattering occurs, the process parameters of the weak rigid parts can be optimized based on the generated stability characteristics, thereby solving the chattering problem and improving the machining efficiency and quality of the workpiece. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] Figure 1 A schematic flowchart illustrating a workpiece machining optimization method provided in an embodiment of this application; Figure 2 A frequency response diagram of a workpiece processing optimization method provided in an embodiment of this application; Figure 3 A schematic diagram of a workpiece processing optimization device provided in an embodiment of this application; Figure 4 This is a structural block diagram of an apparatus for performing a workpiece processing optimization method according to an embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0022] Figure 1 This is a schematic flowchart illustrating a workpiece machining optimization method provided in an embodiment of this application. Figure 1 As shown, the workpiece machining optimization method may include the following steps: S110. Based on the vibration response of the workpiece and the tool under the preset hammering operation, determine the corresponding frequency response function, and based on the amplitude of the frequency response function, determine the weak rigid component from the workpiece and the tool.

[0023] In this context, the workpiece refers to the workpiece to be processed, and the cutting tool refers to the tool used to process the workpiece and the spindle as a whole. By ignoring the interaction between the cutting tool and the spindle, the cutting tool and the spindle are treated as a whole in order to perform chatter analysis.

[0024] The preset hammering operation involves hammering the workpiece and the cutting tool separately through a hammering experiment to determine the vibration response of the workpiece and the cutting tool after hammering. The hammering experiment is a commonly used dynamic characteristic testing method, which refers to the experimental method of applying a short-term impact load to the structure with a specially made hammer by manual or mechanical means, while measuring the vibration response of the structure after the impact (such as displacement, velocity, and acceleration), and then analyzing the dynamic characteristics of the structure.

[0025] Vibration response refers to the vibration parameters generated by a tool or workpiece after being hammered, including parameters such as displacement, velocity, and acceleration. In a hammering test, when a modal hammer strikes a tool or workpiece, the tool or workpiece will vibrate due to the impact load. The set of characteristic parameters such as the amplitude, frequency, and duration of this vibration is the vibration response. The vibration response can transform abstract structural performance into quantifiable and analyzable vibration data, thereby determining the corresponding performance of the tool or workpiece. Therefore, the rigidity of the tool and workpiece can be determined based on the vibration response, so as to identify the corresponding weakly rigid components.

[0026] The frequency response function is used to quantify the output response relationship of a tool or workpiece under input excitation at different frequencies. It can characterize the parameters of the tool and workpiece. For example, the amplitude and phase frequency characteristics of the frequency response function can reveal key parameters such as the natural frequency, damping, and dynamic stiffness of the workpiece or tool. By performing Fourier transform on the vibration response and the hammer excitation source, the frequency response function can be determined based on the frequency domain function obtained from the transform. This allows for the subsequent determination of the corresponding parameters of the tool and workpiece based on the function parameters of the frequency response function, thereby further determining the rigidity of the tool and workpiece.

[0027] Weakly rigid components are those with lower rigidity in the cutting tool and workpiece, meaning their dynamic stiffness is significantly lower than other areas. These components have weaker resistance to deformation and are more prone to large elastic deformation, vibration, or displacement under external loads (such as cutting forces, impact forces, and vibration excitation), resulting in chatter. Chatter can also be called tool bounce, a common vibration phenomenon in machining. It refers to the severe and unstable vibration caused by insufficient rigidity between the cutting tool and the workpiece or unreasonable process parameters during the cutting process, leading to deterioration of the machined surface quality, accelerated tool wear, and even breakage.

[0028] Based on this, by performing a preset hammering operation on the workpiece and the tool, the vibration response of the workpiece and the tool under the hammering source is obtained. Based on the vibration response and the hammering source, the frequency response function of the workpiece and the tool is determined, and the frequency response function with the larger amplitude is identified so that the component corresponding to the frequency response function can be identified as a weak rigid component. Weak rigid components are more prone to chattering in the machining process. Therefore, by identifying weak rigid components, adjustments can be made in a timely manner when they appear, thereby ensuring the normal execution of the machining.

[0029] S120. Collect at least one vibration parameter of the weak rigid component under real-time processing operation, obtain the vibration signal, and determine whether the weak rigid component is vibrating based on the signal characteristic value of the vibration signal and the frequency domain peak value of the corresponding frequency domain signal.

[0030] Vibration parameters are physical quantities that describe the vibration state, characteristics, and laws of an object. By quantifying the intensity, frequency, and morphology of vibration, a scientific description and analysis of vibration phenomena can be achieved. These parameters may include displacement, velocity, and acceleration.

[0031] Vibration signals are electrical signals corresponding to vibration data, such as displacement, velocity, and acceleration. Vibration data is converted into electrical signals (e.g., voltage, current) by sensors, forming analyzable vibration signals. Signal characteristic values ​​are signal values ​​that can express the characteristics of a vibration signal; they are parameters or indicators that quantify the essential characteristics of the signal. Raw vibration signals are usually complex waveform data, making direct analysis difficult. Characteristic values, through mathematical calculations or statistical methods, simplify complex signals into numerical values ​​with clear physical meaning, allowing for rapid identification of key vibration characteristics. For example, the amplitude of a signal can characterize its maximum value, such as maximum acceleration or maximum displacement.

[0032] Frequency domain signal refers to the signal form obtained after converting the vibration signal to the frequency domain, revealing the frequency composition and energy distribution of the signal; the frequency domain peak value is the signal peak value corresponding to the frequency domain signal.

[0033] Chatter refers to the vibration phenomenon that occurs in the cutting tool or workpiece during the machining process. It refers to the severe and unstable vibration caused by insufficient rigidity or unreasonable process parameters between the cutting tool and the workpiece during the cutting process, which leads to the deterioration of the machined surface quality, accelerated tool wear, or even breakage. It can also be understood as the tool bounce phenomenon.

[0034] Based on this, after identifying the weakly rigid components in the tool and workpiece, the vibration parameters of these components are collected during real-time machining to determine the corresponding vibration signals. Further analysis of the amplitude, variance, and other signal characteristics of the vibration signals, as well as the frequency domain peak value of the frequency domain signal obtained after time-frequency transformation, is used to determine whether the weakly rigid components are experiencing chattering. Chattering can lead to reduced machining accuracy, increased wear, and increased safety risks. Therefore, it is necessary to promptly identify whether chattering occurs during machining to eliminate it in a timely manner and improve machining accuracy and efficiency.

[0035] S130. If so, then determine the dynamic model based on the frequency response characteristic value of the weakly rigid component, and perform stability calculation on the dynamic model to obtain a stability characteristic diagram; the frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component.

[0036] Among them, the frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component. It refers to the key characteristic parameter in the frequency response function that can characterize the dynamic characteristics of the weakly rigid component. It can be a quantized parameter extracted from the amplitude, phase and frequency distribution of the frequency response function. For example, the frequency response characteristic value can be the amplitude of the frequency response function, which characterizes the resonance peak of the weakly rigid component, or it can be the frequency corresponding to the amplitude, which characterizes the natural frequency of the weakly rigid component. By determining the characteristic parameter in the frequency response function that can characterize the property characteristics of the weakly rigid component, the frequency response characteristic value is obtained, so that the property characteristics of the weakly rigid component can be determined subsequently based on the frequency response characteristic value.

[0037] A dynamic model is a model that abstracts and quantifies the motion state, force relationships, and dynamic response laws of an object or system through mathematical equations, physical principles, or data fitting. Essentially, it transforms complex physical phenomena (such as vibration, motion, and force transmission) into calculable and analyzable mathematical expressions. Dynamic models can be used to describe the vibration laws of weakly rigid components, thereby helping to determine the optimal process parameters for weakly rigid components. In this way, the optimal process parameters can be used to achieve the best vibration state of the weakly rigid components, thereby improving the efficiency and quality of processing.

[0038] Stability calculation can be performed using semi-discrete or fully discrete methods. These methods determine stability by solving the characteristic equations obtained after discretization. Semi-discrete calculation involves semi-discretizing the continuous cutting process, discretizing the time variable, but retaining some continuity of the system's dynamic equations. Fully discrete calculation completely discretizes the system's dynamic equations (including variables such as vibration displacement and cutting force) in both time and space, and solves for the system's state at discrete moments through numerical iteration (such as the finite difference method), thereby determining whether the system is stable.

[0039] The stability feature diagram is also known as the cutting stability lobe diagram. The cutting stability lobe diagram is a visual chart of the stability boundary generated after analyzing the cutting system (the vibration system composed of the tool, workpiece, and machine tool) using stability calculation methods (such as the semi-discrete method and the fully discretized method). It can be used to intuitively divide the stable and unstable regions in order to determine the optimal process parameters of the workpiece or tool.

[0040] Therefore, if the current weak rigid component experiences chatter, it is necessary to eliminate the chatter to avoid machining abnormalities. This can be achieved by determining the frequency response characteristic value of the frequency response function corresponding to the weak rigid component, thereby generating a dynamic model that can describe the vibration law of the weak rigid component. Furthermore, the stability of the dynamic model can be calculated to obtain a cutting stability lobe diagram. This diagram can then be used to determine the target process parameters corresponding to the weak rigid component and adjust the machining parameters accordingly.

[0041] S140. Based on the characteristic curve in the stable characteristic diagram, determine the target cutting parameters, and adjust the process parameters of the weakly rigid component according to the target cutting parameters.

[0042] Among them, the characteristic curve refers to the stability boundary curve in the cutting stability lobe diagram, also known as the lobe curve. It can be understood as the critical boundary between stability and instability. Each curve in the lobe diagram represents the critical boundary for the cutting system to transition from a stable state to an unstable state. It can be understood that when the actual machining parameters fall exactly on the curve, the system is in a critical stable state. At this time, the vibration of the tool and the workpiece is about to intensify, but there is no obvious chatter yet. When the parameters are below the curve, the system is in the stable region, the vibration is small, the machining process is smooth, and there is no risk of chatter. When the parameters are above the curve, the system enters the unstable region, the vibration is amplified sharply, chatter occurs immediately, and problems such as surface texture, sudden increase in cutting force, and accelerated tool wear are manifested.

[0043] The target cutting parameters can be understood as the optimal combination of cutting parameters to avoid chattering during the cutting process. By determining the target cutting parameters corresponding to the weak rigid component, the weak rigid component can be machined according to the target cutting parameters, thereby eliminating chattering. These parameters may include spindle speed, feed rate, cutting speed, and depth of cut.

[0044] Based on this, by using the stability boundary curve in the cutting stability lobe diagram, the optimal cutting parameters for the weakly rigid component can be determined while ensuring stability. This allows the original process parameters of the weakly rigid component to be adjusted according to the target cutting parameters, thereby eliminating chatter.

[0045] S150. Process the weakly rigid components according to the adjusted process parameters.

[0046] Based on this, by determining the cutting stability lobe diagram, the stability under different cutting speeds and feed rates during the cutting process can be analyzed. In order to adjust the process parameters of weakly rigid parts according to the determined target cutting parameters, and continue to process according to the adjusted process parameters, the cutting efficiency can be improved, the impact and vibration of the tool during the cutting process can be reduced, thereby extending the tool life and improving the machining accuracy.

[0047] Based on the feasible implementation of S110 described above, this application further provides the following steps: determining the corresponding frequency response function based on the vibration response of the workpiece and the tool under a preset hammering operation, and identifying the weakly rigid component from the workpiece and the tool based on the amplitude of the frequency response function; Determine the excitation signal corresponding to the excitation source of the preset hammering operation, and the response signal corresponding to the vibration response; Perform a Fourier transform on the excitation signal to obtain the excitation frequency domain signal, and perform a Fourier transform on the response signal to obtain the response frequency domain signal; The frequency response function is determined by the ratio of the response frequency domain signal to the excitation frequency domain signal.

[0048] The excitation source refers to the dynamic excitation signal applied to the system (such as workpiece, tool, structural component, etc.) through preset parameters or mechanisms during the hammering process. The excitation signal is the excitation source corresponding to the excitation source. For example, if the hammering is repeated on the tool and workpiece at a preset frequency, a periodic excitation is formed, and the corresponding spectrum is mainly composed of the fundamental frequency and harmonics.

[0049] Excitation signal refers to the dynamic force signal output by the hammering device (excitation source) and applied to the tool and workpiece during a pre-planned hammering test or operation.

[0050] The Fourier transform is a transformation that converts a signal from the time domain (a representation that changes over time) to the frequency domain (a representation that is distributed according to frequency) in order to determine the hidden frequency components and features in the signal.

[0051] Based on this, by determining the excitation signal corresponding to the hammering excitation source of the preset hammering operation, as well as the vibration response of the tool and workpiece to the hammering, Fourier transform is performed on the excitation signal and the vibration signal corresponding to the vibration response to obtain the frequency domain signals in the frequency domain, and the frequency response function is determined according to the ratio of the response frequency domain signal to the excitation frequency domain signal.

[0052] Based on the feasible implementation of S120 described above, this application further provides the following steps: signal characteristic values ​​including signal variance; acquiring at least one vibration parameter of a weakly rigid component under real-time processing operation to obtain a vibration signal; and determining whether the weakly rigid component is vibrating based on the signal characteristic values ​​of the vibration signal and the corresponding frequency domain peak value of the frequency domain signal. Calculate the signal variance corresponding to the vibration signal; The frequencies corresponding to each amplitude of the frequency response function are determined as the natural frequencies of the weakly rigid component, resulting in multiple natural frequencies. The peak frequencies corresponding to each frequency domain peak in the frequency domain signal are then determined. The comparison results are obtained by comparing the signal variance with the preset variance, as well as the peak frequency with the natural frequency. Based on the comparison results, determine whether the weakly rigid component is experiencing vibration.

[0053] Among them, signal variance is an important statistic describing the degree of dispersion or fluctuation characteristics of a signal. It reflects the degree of dispersion of the signal around the mean. The larger the variance, the more violent the fluctuation of the signal; the smaller the variance, the more stable the signal and the closer it is to its mean.

[0054] Peak frequency is the frequency corresponding to the peak value of a signal in the frequency domain.

[0055] The preset variance is a pre-determined variance used to determine whether the signal variance meets the actual requirements.

[0056] Based on this, the signal variance corresponding to the vibration signal is calculated, and the corresponding natural frequency is determined according to the frequency corresponding to the amplitude in the frequency response function of the weakly rigid component. The peak frequency is determined according to the frequency corresponding to the amplitude in the frequency domain signal. When the spalling occurs, the vibration of the component is unstable and the signal amplitude changes greatly. The signal variance will increase, and the amplitude is likely to appear at the natural frequency of the component. Therefore, by comparing the signal variance with the preset variance, as well as the peak frequency with the natural frequency, it is possible to determine whether the weakly rigid component is experiencing a spalling phenomenon based on the characteristics of the signal.

[0057] Based on the feasible implementation of S120 described above, this application further provides steps for determining whether a weakly rigid component vibrates based on the comparison results, including: If the comparison result shows that the signal variance is greater than the preset variance and there is a peak frequency in the peak frequency that is the same as any natural frequency, then it is determined that the weak rigid component is vibrating. If the comparison result is that the signal variance is not greater than the preset variance, or there is no peak frequency in the peak frequency that is the same as any natural frequency, then it is determined that the weak rigid component has not vibrated.

[0058] Based on this, the signal variance and the peak frequency of the frequency domain signal are used to determine whether the weak rigid component is fluctuating. If the signal variance is greater than the preset variance and there is a peak frequency in the peak frequency that is the same as any natural frequency, that is, a specific frequency peak appears in the signal and these frequencies are close to the natural frequency of the system, then it is determined that fluctuating has occurred. Otherwise, it is determined that no fluctuating has occurred.

[0059] Based on the feasible implementation of S130 described above, this application further provides the following steps: determining a dynamic model based on the frequency response characteristic value of the weakly rigid component, and performing stability calculations on the dynamic model to obtain a stability characteristic diagram, including: Determine multiple amplitude values ​​of the frequency response function corresponding to the weakly rigid component; Based on the frequencies corresponding to each amplitude, determine multiple natural frequencies in the frequency response characteristic value; The half-power bandwidth of each amplitude is calculated to obtain multiple damping coefficients in the frequency response characteristic value; Based on the amplitude and the mass of the weakly rigid component, determine multiple stiffness values ​​in the frequency response characteristic values; Substituting the natural frequencies, damping coefficients, and stiffness into the vibration rules yields the dynamic model.

[0060] The vibration rule can be understood as a vibration differential equation, which is used to determine the dynamic model. By determining the characteristic values ​​such as natural frequency, damping coefficient and stiffness, these values ​​are substituted into the vibration differential variance to obtain the dynamic model. The characteristic values ​​such as natural frequency, damping coefficient and stiffness can be determined by the frequency response function.

[0061] Half-power bandwidth is a key indicator for describing the resonance characteristics of a system. In a vibration system, damping is the core factor that causes the change in the width of the resonance peak. The greater the damping, the flatter the resonance peak and the wider the half-power bandwidth; the smaller the damping, the sharper the resonance peak and the narrower the half-power bandwidth. Therefore, this characteristic can be used to inversely deduce the damping coefficient from the half-power bandwidth.

[0062] Stiffness is a physical quantity that describes the ability of an object or structure to resist deformation. The greater the stiffness, the smaller the deformation of the object under the same external force; conversely, the smaller the stiffness, the more significant the deformation.

[0063] Based on this, the amplitude of the vibration response of a weakly rigid component during processing is directly related to parameters such as stiffness and damping, and the frequency corresponding to the amplitude is the natural frequency of the weakly rigid component. Therefore, by determining the amplitude of the frequency response function, multiple frequency response characteristic values ​​such as natural frequency, stiffness, and damping coefficient can be determined, so as to be substituted into the vibration rules to obtain the dynamic model.

[0064] Based on the feasible implementation of S140 described above, this application further provides the following steps: vibration parameters including cutting speed; determining target cutting parameters based on the characteristic curve in the stability feature map; and adjusting the process parameters of the weakly rigid component based on the target cutting parameters. Determine the horizontal and vertical axes of the stable feature map; the horizontal axis is used to characterize the cutting speed corresponding to the weak rigid component, and the vertical axis is used to characterize the cutting depth corresponding to the weak rigid component. Based on the corresponding horizontal and vertical coordinates of the characteristic curve, the target cutting depth corresponding to each cutting speed of the weak rigid component is determined, and the corresponding real-time cutting depth is determined from the target cutting depth based on the real-time cutting speed of the weak rigid component under real-time machining operation. Based on the real-time cutting speed and real-time cutting depth, the target cutting parameters are determined, and the process parameters of the weakly rigid parts are adjusted according to the target cutting parameters.

[0065] The target cutting depth refers to the cutting depth on the characteristic curve corresponding to the cutting speed.

[0066] Based on this, the horizontal axis in the blade diagram usually represents the cutting width during the cutting process, the vertical axis represents the rotational speed of the machine tool spindle, the area inside the curve represents an unstable cutting state (prone to chatter), and the blank area outside the blade represents a stable cutting state. Therefore, by using the coordinates corresponding to the curve, the optimal process parameters of the weakly rigid component can be determined under the premise of ensuring stability.

[0067] Based on the feasible implementation of S150 described above, this application further provides the following steps after determining the target cutting parameters based on the characteristic curve in the stable feature map, and adjusting the process parameters of the weakly rigid component according to the target cutting parameters: Strengthen the stiffness of weakly rigid components.

[0068] Among them, stiffness enhancement treatment refers to the treatment method of improving the deformation resistance of weak rigid components by adjusting materials, optimizing structures, or adding auxiliary measures. For example, stiffness can be directly improved by replacing or reinforcing materials, stiffness efficiency can be improved by geometric design, or external support can be provided for weak rigid components.

[0069] Based on this, in practical applications, targeted stiffening improvements can be made to weakly rigid components to increase local rigidity and further improve the processing stability limit.

[0070] Please refer to Figure 2 , Figure 2 This is a frequency response diagram illustrating a workpiece machining optimization method provided in this application embodiment; taking the machining of a certain part with a spring tool as an example, a serious quality problem occurred on the machined surface; to address this problem, modal analysis was performed on both the workpiece area and the tool-spindle subsystem area, as follows... Figure 2 As shown, the radial frequency response amplitude of the workpiece is found to be the largest, identifying it as a weakly rigid part. Based on this, the machining process signals were analyzed. Waveform observation and maximum value detection revealed periodic large-amplitude vibrations in the acceleration signal, with some signals exceeding the sensor's range, indicating abnormal vibration. Analysis of the sound signal also showed large vibrations similar to the acceleration, confirming a tool chatter problem. Further acquisition of the time-domain frequency response data and analysis of the acceleration and sound frequency domain signals identified maximum amplitudes at 712.4Hz and 710.9Hz. Figure 2 The fourth mode of the component at 712.77Hz is basically the same, indicating that a resonance problem occurred near this frequency, which further verifies the feasibility of the previous time-domain signal identification.

[0071] To address the aforementioned tool bounce problem, stiffness was increased in the corresponding weak rigidity areas, and the process parameters were adjusted. Further data collection of the machining process signals revealed that the vibration was relatively stable in the time domain signal, and the frequency components in the frequency domain signal were mainly the spindle frequency and its multiples, with no chatter frequency. This indicates that the machining process was stable and the machined surface quality was good.

[0072] In existing technologies, thin-walled aircraft parts are widely used in the aerospace field due to their light weight, compact structure, and excellent performance. However, during the machining of these parts, improper selection of process parameters often leads to tool bounce problems, severely affecting machining quality and efficiency. Currently, to effectively improve this problem, the main approach is to reduce the spindle speed, forcing the cutting excitation frequency to deviate from the part's natural frequency, thereby avoiding resonance. While this method has some positive effect on improving the tool bounce problem, it cannot truly and effectively solve the problem, especially for serious quality issues that occur in actual machining, such as surface deterioration and tool breakage.

[0073] In some embodiments of this application, a hammering test is performed on the workpiece and the cutting tool to obtain their vibration response under the hammering excitation source. This allows for Fourier transform of the vibration signal corresponding to the vibration response and the excitation signal from the hammering excitation source to obtain the frequency domain vibration signal and frequency domain excitation signal. This further determines the frequency response function of the cutting tool and the workpiece. Based on the amplitude of the frequency response function, weak rigid components in the cutting tool and the workpiece can be identified, enabling timely adjustment of these components when chatter occurs. Furthermore, the vibration parameters of the weak rigid components during the machining process are collected in real time to obtain the corresponding vibration signal, and the impact of the vibration signal on the machine is determined. The signal variance and peak frequency of the frequency domain signal are compared with the preset variance, peak frequency and natural frequency of the weak rigid component to determine whether the weak rigid component exhibits chattering. If so, the dynamic model is determined by the frequency response characteristic value of the weak rigid component, and the stability of the dynamic model is calculated to obtain the cutting stability lobe diagram. Based on the curve in the cutting stability lobe diagram, the target cutting parameters corresponding to the weak rigid component are determined under the premise of ensuring machining stability. The machining parameters are then adjusted according to the target cutting parameters so that the weak rigid component can be machined according to the adjusted process parameters.

[0074] Thus, by fusing signals from multiple sensors (such as accelerometers, force sensors, and sound pressure sensors), various dynamic information during the machining process can be comprehensively captured. The complementarity and redundancy of multi-source signals can effectively reduce the risk of misjudgment caused by a single signal. Modal analysis of the tool and workpiece is performed to obtain their frequency response functions, thereby identifying weak rigidity parts. By combining time-domain and frequency-domain signals, vibration during the machining process can be comprehensively monitored. Based on multi-dimensional signals, the accuracy and reliability of identifying tool chatter are improved. At the same time, by combining dynamic theory analysis and stability calculation, a cutting stability lobe diagram is generated to determine the target machining parameters. The stiffness of weak rigidity parts is increased, and a comprehensive improvement measure of process parameter optimization and stiffness increase synergistic control is proposed. This approach is applicable to the root cause location of serious quality problems caused by tool chatter on-site and can effectively solve such problems, ensuring machining quality.

[0075] Figure 3 This is a schematic diagram of a workpiece processing optimization device 300 provided in an embodiment of this application. Figure 3 As shown, the workpiece processing optimization device 300 includes: a determining module 310, a judging module 320, a calculating module 330, an adjusting module 340, and a processing module 350; wherein: The determination module 310 is used to determine the corresponding frequency response function based on the vibration response of the workpiece and the tool under the preset hammering operation, and to determine the weak rigid component from the workpiece and the tool based on the amplitude of the frequency response function. The judgment module 320 is used to collect at least one vibration parameter of the weak rigid component under real-time processing operation, obtain the vibration signal, and determine whether the weak rigid component is vibrating based on the signal characteristic value of the vibration signal and the frequency peak value of the corresponding frequency domain signal. The calculation module 330 is used to determine the dynamic model based on the frequency response characteristic value of the weakly rigid component if the condition is met, and to perform stability calculation on the dynamic model to obtain a stability characteristic map; the frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component. The adjustment module 340 is used to determine the target cutting parameters based on the characteristic curve in the stable feature map, and to adjust the process parameters of the weakly rigid component based on the target cutting parameters. The machining module 350 is used to machine weakly rigid components according to the adjusted process parameters.

[0076] In this embodiment of the application, the acquisition module 310 can also be specifically used for: Determine the excitation signal corresponding to the excitation source of the preset hammering operation, and the response signal corresponding to the vibration response; Perform a Fourier transform on the excitation signal to obtain the excitation frequency domain signal, and perform a Fourier transform on the response signal to obtain the response frequency domain signal; The frequency response function is determined by the ratio of the response frequency domain signal to the excitation frequency domain signal.

[0077] In this embodiment of the application, the determination module 320 can also be specifically used for: Calculate the signal variance corresponding to the vibration signal; The frequencies corresponding to each amplitude of the frequency response function are determined as the natural frequencies of the weakly rigid component, resulting in multiple natural frequencies. The peak frequencies corresponding to each frequency domain peak in the frequency domain signal are then determined. The comparison results are obtained by comparing the signal variance with the preset variance, as well as the peak frequency with the natural frequency. Based on the comparison results, determine whether the weakly rigid component is experiencing vibration.

[0078] In this embodiment of the application, the determination module 320 can also be specifically used for: If the comparison result shows that the signal variance is greater than the preset variance and there is a peak frequency in the peak frequency that is the same as any natural frequency, then it is determined that the weak rigid component is vibrating. If the comparison result is that the signal variance is not greater than the preset variance, or there is no peak frequency in the peak frequency that is the same as any natural frequency, then it is determined that the weak rigid component has not vibrated.

[0079] In this embodiment of the application, the calculation module 330 can also be specifically used for: Determine multiple amplitude values ​​of the frequency response function corresponding to the weakly rigid component; Based on the frequencies corresponding to each amplitude, determine multiple natural frequencies in the frequency response characteristic value; The half-power bandwidth of each amplitude is calculated to obtain multiple damping coefficients in the frequency response characteristic value; Based on the amplitude and the mass of the weakly rigid component, determine multiple stiffness values ​​in the frequency response characteristic values; Substituting the natural frequencies, damping coefficients, and stiffness into the vibration rules yields the dynamic model.

[0080] In this embodiment of the application, the adjustment module 340 can also be specifically used for: Determine the horizontal and vertical axes of the stable feature map; the horizontal axis is used to characterize the cutting speed corresponding to the weak rigid component, and the vertical axis is used to characterize the cutting depth corresponding to the weak rigid component. Based on the corresponding horizontal and vertical coordinates of the characteristic curve, the target cutting depth corresponding to each cutting speed of the weak rigid component is determined, and the corresponding real-time cutting depth is determined from the target cutting depth based on the real-time cutting speed of the weak rigid component under real-time machining operation. Based on the real-time cutting speed and real-time cutting depth, the target cutting parameters are determined, and the process parameters of the weakly rigid parts are adjusted according to the target cutting parameters.

[0081] In this embodiment of the application, the processing module 350 can also be specifically used for: Strengthen the stiffness of weakly rigid components.

[0082] Figure 4 This is a schematic diagram of the structure of an apparatus for performing a workpiece processing optimization method according to an embodiment of this application. Figure 4 As shown, the device 400 includes: The device 400 may include a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a communication component 403, and other components. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0083] In the specific implementation process, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to execute the above-mentioned workpiece processing optimization method.

[0084] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0085] Furthermore, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0086] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0087] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0088] In some embodiments, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps in the machining optimization method for any of the above-described workpieces.

[0089] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0090] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0091] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of program codes, which can be loaded by a processor to execute the steps in any of the workpiece processing optimization methods provided in embodiments of this application.

[0092] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0093] According to one aspect of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium.

[0094] Since the instructions stored in the storage medium can execute the steps in any of the workpiece processing optimization methods provided in the embodiments of this application, the beneficial effects that any of the workpiece processing optimization methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0095] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the appended claims.

[0096] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for optimizing the machining of a workpiece, characterized in that, The method includes: Based on the vibration response of the workpiece and the cutting tool under a preset hammering operation, the corresponding frequency response function is determined, and based on the amplitude of the frequency response function, the weak rigid component is determined from the workpiece and the cutting tool. At least one vibration parameter of the weakly rigid component is collected during real-time processing to obtain a vibration signal. Based on the signal characteristic value of the vibration signal and the frequency domain peak value of the corresponding frequency domain signal, it is determined whether the weakly rigid component is fluttering. The signal characteristic value includes the signal variance. The process involves acquiring at least one vibration parameter of the weakly rigid component during real-time processing to obtain a vibration signal. Based on the signal characteristic values ​​of the vibration signal and the corresponding frequency domain peak value, it is determined whether the weakly rigid component is experiencing vibration, including: Calculate the signal variance corresponding to the vibration signal; The frequencies corresponding to each amplitude value of the frequency response function are determined as the natural frequencies of the weakly rigid component, thereby obtaining a plurality of natural frequencies, and the peak frequencies corresponding to each frequency domain peak value in the frequency domain signal are determined. The comparison results are obtained by comparing the signal variance with the preset variance, and the peak frequency with the natural frequency. Based on the comparison results, it is determined whether the weakly rigid component is experiencing vibration. If so, then based on the frequency response characteristic value of the weakly rigid component, a dynamic model is determined, and the stability of the dynamic model is calculated to obtain a stability characteristic map; the frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component. Based on the characteristic curves in the stability feature map, the target cutting parameters are determined, and the process parameters of the weakly rigid component are adjusted according to the target cutting parameters. The weakly rigid component is processed according to the adjusted process parameters.

2. The method according to claim 1, characterized in that, The step of determining the corresponding frequency response function based on the vibration response of the workpiece and the cutting tool under a preset hammering operation, and determining the weakly rigid component from the workpiece and the cutting tool based on the amplitude of the frequency response function, includes: Determine the excitation signal corresponding to the excitation source of the preset hammering operation, and the response signal corresponding to the vibration response; Perform a Fourier transform on the excitation signal to obtain the excitation frequency domain signal, and perform a Fourier transform on the response signal to obtain the response frequency domain signal; The frequency response function is determined based on the ratio of the response frequency domain signal to the excitation frequency domain signal.

3. The method according to claim 1, characterized in that, The step of determining whether the weakly rigid component vibrates based on the comparison result includes: If the comparison result is that the signal variance is greater than the preset variance, and there is a peak frequency in the peak frequency that is the same as any of the inherent frequencies, then it is determined that the weak rigid component is vibrating. If the comparison result is that the signal variance is not greater than the preset variance, or there is no peak frequency among the peak frequencies that is the same as any of the inherent frequencies, then it is determined that the weakly rigid component has not vibrated.

4. The method according to claim 1, characterized in that, The process of determining a dynamic model based on the frequency response characteristic value of the weakly rigid component, and performing stability calculations on the dynamic model to obtain a stability characteristic map, includes: Determine multiple amplitude values ​​of the frequency response function corresponding to the weakly rigid component; Based on the frequencies corresponding to each amplitude, determine multiple inherent frequencies in the frequency response characteristic values; The half-power bandwidth of each amplitude is calculated to obtain multiple damping coefficients in the frequency response characteristic value; Based on each of the amplitude values ​​and the mass of the weakly rigid component, multiple stiffness values ​​in the frequency response characteristic values ​​are determined; Substituting the natural frequencies, damping coefficients, and stiffness values ​​into the vibration rules yields the dynamic model.

5. The method according to claim 1, characterized in that, The vibration parameters include the cutting speed; The step of determining the target cutting parameters based on the characteristic curves in the stable feature map, and adjusting the process parameters of the weakly rigid component based on the target cutting parameters, includes: Determine the horizontal and vertical axes of the stability feature map; the horizontal axis is used to characterize the cutting speed corresponding to the weak rigid component, and the vertical axis is used to characterize the cutting depth corresponding to the weak rigid component; Based on the horizontal and vertical coordinates corresponding to the feature curves, the target cutting depth corresponding to each cutting speed of the weak rigid component is determined, and based on the real-time cutting speed of the weak rigid component under real-time machining operation, the corresponding real-time cutting depth is determined from the target cutting depth. Based on the real-time cutting speed and the real-time cutting depth, the target cutting parameters are determined, and the process parameters of the weakly rigid component are adjusted according to the target cutting parameters.

6. The method according to claim 1, characterized in that, After determining the target cutting parameters based on the characteristic curves in the stability feature map, and adjusting the process parameters of the weakly rigid component based on the target cutting parameters, the method further includes: The weakly rigid component is subjected to stiffness enhancement treatment.

7. A workpiece processing optimization device, characterized in that, The device includes: The determination module is used to determine the corresponding frequency response function based on the vibration response of the workpiece and the tool under a preset hammering operation, and to determine the weak rigid component from the workpiece and the tool based on the amplitude of the frequency response function. The judgment module is used to collect at least one vibration parameter of the weakly rigid component under real-time processing operation, obtain a vibration signal, and determine whether the weakly rigid component is fluttering based on the signal characteristic value of the vibration signal and the frequency domain peak value of the corresponding frequency domain signal; wherein, the signal characteristic value includes the signal variance; The process involves acquiring at least one vibration parameter of the weakly rigid component during real-time processing to obtain a vibration signal. Based on the signal characteristic values ​​of the vibration signal and the corresponding frequency domain peak value, it is determined whether the weakly rigid component is experiencing vibration, including: Calculate the signal variance corresponding to the vibration signal; The frequencies corresponding to each amplitude value of the frequency response function are determined as the natural frequencies of the weakly rigid component, thereby obtaining a plurality of natural frequencies, and the peak frequencies corresponding to each frequency domain peak value in the frequency domain signal are determined. The comparison results are obtained by comparing the signal variance with the preset variance, and the peak frequency with the natural frequency. Based on the comparison results, it is determined whether the weakly rigid component is experiencing vibration. The calculation module is used to determine the dynamic model based on the frequency response characteristic value of the weakly rigid component if the condition is met, and to perform stability calculation on the dynamic model to obtain a stability characteristic map; the frequency response characteristic value is the frequency response characteristic value corresponding to the frequency response function of the weakly rigid component. The adjustment module is used to determine the target cutting parameters based on the feature curves in the stable feature map, and to adjust the process parameters of the weakly rigid component based on the target cutting parameters. The processing module is used to process the weakly rigid component according to the adjusted process parameters.

8. A device, characterized in that, include: One or more processors; Memory; One or more programs, wherein the one or more programs are stored in memory and configured to be executed by one or more processors, the one or more programs being configured to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be called by a processor to perform the method as described in any one of claims 1 to 6.

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