Thin-walled workpiece milling machining control method, device, equipment and medium

Abstract

The application relates to a thin-walled workpiece milling machining control method, device, equipment and medium, the method comprising the following steps: based on the total cutting material volume and the total energy consumption, a system control parameter optimization model for minimizing the total energy consumption and maximizing the total cutting material volume is constructed; the system control parameter optimization model is solved based on a preset whale optimization algorithm to determine optimal system control parameters, wherein the system control parameters represent a system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control; based on the optimal spindle speed corresponding to the spindle of the thin-walled workpiece and the optimal system control parameters, the thin-walled workpiece to be milled is controlled to perform machining, so as to complete the processing control of thin-walled workpiece milling. The application can automatically optimize the PID controller parameters and the feedforward control, and improve the control accuracy and response speed of the system.

Description

Technical Field

[0001] This application relates to the field of parts processing, and in particular to a method for controlling the milling of thin-walled workpieces, a corresponding device, electronic equipment, and a computer-readable storage medium. Background Technology

[0002] Thin-walled workpieces, characterized by high strength and lightweight, are widely used in the aerospace field, such as in critical components like aircraft frames and fuselages. However, milling thin-walled workpieces is prone to chatter, a phenomenon that occurs when the cutting force between the tool and the thin-walled workpiece reaches a certain critical value, causing unstable, self-excited vibrations. This phenomenon not only increases the surface roughness of the machined surface but also leads to dimensional deviations and decreased machining accuracy. Several factors contribute to chatter, including tool rigidity, workpiece clamping method, selection of cutting parameters, machine tool dynamic characteristics, and the stability of the machining environment.

[0003] Traditional milling methods for thin-walled workpieces typically require multiple trial cuts and adjustments on actual machine tools to determine the optimal machining parameters and process flow. This method not only consumes a large amount of machine tool resources, but also has a low success rate due to the complex shape and material properties of thin-walled workpieces, requiring repeated adjustments and trials, which consumes a lot of time and human resources. At the same time, the machining process is easily affected by problems such as vibration and workpiece deformation, which further increases the difficulty and risk of machining.

[0004] In summary, the traditional milling methods for thin-walled workpieces in the existing technology usually require multiple trial cuts and adjustments on actual machine tools, which not only consumes a lot of machine tool resources, but also, due to the complex shape and material properties of thin-walled workpieces, the success rate of processing is often not high, requiring repeated adjustments and experiments, which consumes a lot of time and human resources. The applicant has made corresponding explorations to solve this problem. Summary of the Invention

[0005] The purpose of this application is to solve the above-mentioned problems by providing a method for controlling the milling of thin-walled workpieces, a corresponding device, electronic equipment, and a computer-readable storage medium.

[0006] To achieve the various objectives of this application, the following technical solution is adopted:

[0007] A method for controlling the milling of thin-walled workpieces, proposed to meet one of the purposes of this application, includes:

[0008] In response to the vibration suppression command during milling of thin-walled workpieces, the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force are determined, wherein the system transfer function is constructed from the system natural frequency and the system damping ratio;

[0009] Based on the spindle speed control model that minimizes system vibration, the optimal spindle speed corresponding to the spindle of the thin-walled workpiece is determined according to the spectrum function, system transfer function, system damping ratio and system damping frequency corresponding to the cutting force.

[0010] In response to the system control parameter optimization command, the total cutting material volume and total energy consumption of the thin-walled workpiece are determined. Based on the total cutting material volume and the total energy consumption, a system control parameter optimization model is constructed to minimize the total energy consumption and maximize the total cutting material volume.

[0011] The system control parameter optimization model is solved based on the preset whale optimization algorithm to determine the optimal system control parameters, wherein the system control parameters represent the system control parameter combination constructed by proportional gain, integral gain, derivative gain and feedforward control;

[0012] Based on the optimal spindle speed and optimal system control parameters corresponding to the thin-walled workpiece spindle, the thin-walled workpiece to be milled is controlled to perform machining, thereby completing the machining control of thin-walled workpiece milling.

[0013] Optionally, the steps for constructing the spindle speed control model include:

[0014] Perform a fast Fourier transform on the cutting force to determine the corresponding spectral function of the cutting force;

[0015] The dynamic stiffness between the thin-walled workpiece and the cutting tool, as well as the equivalent mass of the thin-walled workpiece and the cutting tool, are determined in a simplified two-degree-of-freedom vibration model. Based on the dynamic stiffness between the thin-walled workpiece and the cutting tool, as well as the equivalent mass of the thin-walled workpiece and the cutting tool, the natural frequency of the system is determined.

[0016] Determine the system damping ratio, and calculate the system damping frequency based on the system's natural frequency and the system damping ratio;

[0017] The system's transfer function is constructed based on the system's natural frequency and damping ratio.

[0018] Based on the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force, a spindle speed control model is constructed to minimize system vibration.

[0019] Optionally, the simplified two-degree-of-freedom vibration model can be expressed as follows:

[0020]

[0021] Among them, among them, This represents the vibration displacement of the thin-walled workpiece at time t. The vibration velocity of the thin-walled workpiece is represented by x(t), the vibration acceleration of the thin-walled workpiece is represented by m, the equivalent mass of the thin-walled workpiece and the cutting tool is represented by c, the damping coefficient is represented by k, the dynamic stiffness between the thin-walled workpiece and the cutting tool is represented by F(t), and the cutting force is represented by F(t).

[0022] Optionally, the expression for the frequency spectrum function corresponding to the cutting force is:

[0023] F(w) = FFT(F(t)),

[0024] Where F(w) represents the frequency spectrum function corresponding to the cutting force;

[0025] The expression for the system's natural frequency is:

[0026]

[0027] Among them, w n Indicates the system's natural frequency;

[0028] The expression for the system damping frequency is:

[0029]

[0030] Among them, w d ζ represents the system damping frequency, and ζ represents the system damping ratio.

[0031] The expression for the transfer function of the system is:

[0032]

[0033] Where w represents the spindle speed;

[0034] The expression for the spindle speed control model that minimizes system vibration is as follows:

[0035] min w |H(w)F(w)|.

[0036] Optionally, the step of determining the total cutting material volume and total energy consumption of the thin-walled workpiece, and constructing a system control parameter optimization model based on the total cutting material volume and the total energy consumption to minimize the total energy consumption and maximize the total cutting material volume includes:

[0037] The objective function for the total cut material volume of the thin-walled workpiece is:

[0038]

[0039] Where A(t) is the cutting area that changes with time, and M... total This indicates the total volume of material cut from a thin-walled workpiece;

[0040] The objective function for the total energy consumption of the thin-walled workpiece is:

[0041]

[0042] Among them, E waste S(t) represents the total energy consumption of the thin-walled workpiece, and S(t) is the spindle speed that varies with time. op This is the optimal speed. When the spindle speed exceeds this optimal speed, it will lead to a waste of power resources.

[0043] The expression for the system control parameter optimization model is:

[0044] f(x) = M total +E waste .

[0045] Optionally, the step of solving the system control parameter optimization model based on a preset whale optimization algorithm to determine the optimal system control parameters includes:

[0046] The preset whale optimization algorithm is invoked, and the system control parameter optimization model is solved based on the whale optimization algorithm to determine the initial parameters of the whale optimization algorithm, including the population size and the number of iterations;

[0047] The whale population is randomly initialized according to the population size to generate initial whale individuals, wherein the whale individual is characterized by a combination of system control parameters constructed by proportional gain, integral gain, differential gain and feedforward control.

[0048] For each iteration step, the fitness objective function value of each individual whale is calculated, and the position and velocity of each individual whale are updated based on the fitness objective function value;

[0049] During the algorithm's iteration process, the whale individual with the optimal fitness objective function value is selected as the optimal combination of system control parameters;

[0050] When the preset number of iterations is reached or the convergence condition is met, the optimization process ends, and the optimal combination of system control parameters constructed by proportional gain, integral gain, derivative gain and feedforward control is obtained.

[0051] Optionally, the optimal rotational speed is 2000 r / min.

[0052] A control device for milling thin-walled workpieces, provided for another purpose of this application, includes:

[0053] The data acquisition module is configured to respond to the vibration suppression command during thin-walled workpiece milling, and determine the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force, wherein the system transfer function is constructed from the system natural frequency and the system damping ratio;

[0054] The optimal spindle speed determination module is configured to determine the optimal spindle speed corresponding to the spindle of the thin-walled workpiece based on the spindle speed control model that minimizes system vibration, according to the spectrum function corresponding to the cutting force, the system transfer function, the system damping ratio, and the system damping frequency.

[0055] The parameter optimization model construction module is configured to respond to system control parameter optimization instructions, determine the total cutting material volume and total energy consumption of the thin-walled workpiece, and construct a system control parameter optimization model based on the total cutting material volume and the total energy consumption to minimize the total energy consumption and maximize the total cutting material volume.

[0056] The optimal parameter combination determination module is configured to solve the system control parameter optimization model based on a preset whale optimization algorithm to determine the optimal system control parameters, wherein the system control parameters represent the system control parameter combination constructed by proportional gain, integral gain, derivative gain and feedforward control;

[0057] The milling control module is configured to control the thin-walled workpiece to be milled based on the optimal spindle speed and optimal system control parameters corresponding to the spindle of the thin-walled workpiece, so as to complete the milling control of the thin-walled workpiece.

[0058] An electronic device provided for another purpose of this application includes a central processing unit and a memory, the central processing unit being configured to invoke and run a computer program stored in the memory to perform the steps of the thin-walled workpiece milling control method of this application.

[0059] A computer-readable storage medium is provided for another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the thin-walled workpiece milling control method, which, when called by a computer, executes the steps included in the corresponding method.

[0060] Compared to existing technologies, this application addresses the problems of traditional thin-walled workpiece milling methods, which typically require multiple trial cuts and adjustments on actual machine tools. This not only consumes significant machine tool resources but also results in a low success rate due to the complex shape and material properties of thin-walled workpieces, necessitating repeated adjustments and trials, and wasting considerable time and human resources. This application offers the following advantages, including but not limited to:

[0061] Firstly, this application constructs a milling framework for thin-walled workpieces based on digital twins. This framework utilizes virtual-real combination technology to ensure the efficiency and quality of thin-walled workpieces in actual processing scenarios.

[0062] Secondly, this application designs a spindle speed control model for thin-walled workpieces. This model effectively reduces chatter and deformation, improves machining accuracy, and enhances overall machining efficiency and product qualification rate by adjusting the dynamic control method of spindle speed.

[0063] Third, this application constructs a method for solving the system control parameter optimization model based on a preset whale optimization algorithm. This method is based on the powerful global search capability and fast convergence characteristics of the whale optimization algorithm, which can automatically optimize PID controller parameters and feedforward control, thereby improving the control accuracy and response speed of the system.

[0064] Fourth, the method of solving the system control parameter optimization model based on the preset whale optimization algorithm in this application can effectively improve cutting efficiency and reduce energy waste by determining the optimal combination of PID controller parameters and feedforward control. Attached Figure Description

[0065] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0066] Figure 1 An exemplary network architecture is used in the thin-walled workpiece milling control system of this application;

[0067] Figure 2 This is a flowchart illustrating the control method for milling thin-walled workpieces in an embodiment of this application.

[0068] Figure 3 This is a schematic diagram illustrating the construction of the spindle speed control model in an embodiment of this application;

[0069] Figure 4 This is a flowchart illustrating the spindle speed control system based on a PID controller and a feedforward control module in an embodiment of this application.

[0070] Figure 5 This is a schematic diagram showing the curve of the initial wall thickness of a thin-walled workpiece changing over time in an embodiment of this application, where feedforward control is combined with PID control.

[0071] Figure 6 This is a schematic diagram of the spindle speed of a thin-walled workpiece changing over time in an embodiment of this application, where feedforward control is combined with PID control.

[0072] Figure 7 This is a schematic diagram of the curve showing the final wall thickness of a thin-walled workpiece changing over time in an embodiment of this application, where feedforward control is combined with PID control.

[0073] Figure 8 This is a schematic diagram of the initial wall thickness changing over time under simple PID control in an embodiment of this application.

[0074] Figure 9 This is a schematic diagram of the spindle speed changing over time under simple PID control in an embodiment of this application.

[0075] Figure 10 This is a schematic diagram of the final wall thickness changing over time under simple PID control in an embodiment of this application.

[0076] Figure 11 This is a schematic block diagram of the thin-walled workpiece milling control device in the embodiments of this application;

[0077] Figure 12 This is a schematic diagram of the structure of the computer device in the embodiments of this application. Detailed Implementation

[0078] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0079] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0080] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0081] Those skilled in the art will understand that the terms "client," "terminal," and "terminal device" as used herein include both devices that receive wireless signals, devices that only possess wireless signal receiver capabilities without transmission capabilities, and devices with receiving and transmitting hardware, devices that have receiving and transmitting hardware capable of bidirectional communication over a bidirectional communication link. Such devices may include: cellular or other communication devices such as personal computers or tablets, having single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service) that can combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant) that may include a radio frequency receiver, pager, internet / intranet access, web browser, notepad, calendar, and / or GPS (Global Positioning System) receiver; and conventional laptops and / or handheld computers or other devices that have and / or include radio frequency receivers. As used herein, "client," "terminal," and "terminal device" can be portable, transportable, installed in a means of transportation (air, sea, and / or land), or suitable and / or configured to operate locally and / or in a distributed manner, operating in any other location on Earth and / or in space. "Client," "terminal," and "terminal device" as used herein can also be a communication terminal, an internet access terminal, or a music / video playback terminal, such as a PDA, a MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or a smart TV, set-top box, etc.

[0082] The hardware referred to by the names "server," "client," and "service node" in this application is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components revealed by the von Neumann architecture, such as a central processing unit (including an arithmetic logic unit and a control unit), memory, input devices, and output devices. The computer program is stored in its memory, and the central processing unit loads the program stored in the secondary storage into the main memory to run it, execute the instructions in the program, and interact with the input and output devices to complete specific functions.

[0083] It should be noted that the concept of "server" used in this application can also be extended to the case of server clusters. Based on the network deployment principles understood by those skilled in the art, the servers should be logically divided. Physically, these servers can be independent of each other but accessible through interfaces, or they can be integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method in this application.

[0084] One or more of the technical features of this application, unless explicitly specified herein, can be deployed on a server and accessed by a client remotely calling the online service interface provided by the server, or can be directly deployed and run on a client for access.

[0085] Unless otherwise specified, the neural network models referenced or potentially referenced in this application may be deployed on a remote server and invoked remotely on the client, or deployed on a client with the capability to invoke directly. In some embodiments, when running on the client, the corresponding intelligence may be acquired through transfer learning in order to reduce the requirements on the client's hardware resources and avoid excessive consumption of the client's hardware resources.

[0086] Unless otherwise specified, all data involved in this application may be stored remotely on a server or on a local terminal device, as long as it is suitable for use by the technical solution of this application.

[0087] Those skilled in the art will understand that although the various methods in this application are described based on the same concept and thus present commonality among them, they can be performed independently unless otherwise specified. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept; therefore, concepts expressed in the same way, as well as concepts that are appropriately changed for convenience but are expressed differently, should be understood equivalently.

[0088] Unless otherwise expressly stated, the various embodiments disclosed in this application can be combined in a cross-cutting manner to flexibly construct new embodiments, as long as such combination does not depart from the inventive spirit of this application and can meet the needs of the prior art or solve a certain deficiency in the prior art. Those skilled in the art should be aware of such modifications.

[0089] Please see Figure 1The thin-walled workpiece milling control method of this application can be executed based on a digital twin-based thin-walled workpiece milling control system. This thin-walled workpiece milling control system combines digital twin technology, Internet of Things technology, electrical control, dynamic model and other technologies to reduce chatter in the thin-walled workpiece milling process, thereby improving the machining quality. The thin-walled workpiece milling control system includes a physical layer, a virtual layer, a dynamic model and a PID controller, etc. The thin-walled workpiece milling control system can acquire, store and analyze the data of the thin-walled machining system in real time, simulate the motion process of thin-walled machining, realize seamless connection and data interaction between the real system and the digital model, thereby improving the quality of thin-walled workpieces.

[0090] In some embodiments, the physical layer partially displays a real thin-wall machining scenario. Through the cooperation of mechanical, electrical, and control systems, the data acquisition system uses torque, pressure, displacement, and temperature sensors to collect various parameters in real time during the milling process and feeds the data back to the control system and virtual model to form a closed-loop control.

[0091] In some embodiments, the virtual layer partially displays a virtual thin-wall machining scenario. Through the combined action of virtual components such as virtual drives, actuators, and virtual tools, a complete thin-wall machining scenario is simulated. This virtual machining scenario can realistically reproduce various situations in actual machining, thereby providing accurate data and feedback for actual machining.

[0092] In some embodiments, the dynamic model continuously adjusts and optimizes its parameters using data provided by the physical and virtual models to reduce chatter during the milling process and improve the accuracy and reliability of the system.

[0093] In some embodiments, the PID controller receives data from the physical layer model and the virtual layer model and makes real-time adjustments to ensure a high degree of synchronization and coordination between the virtual and physical systems.

[0094] Based on the above exemplary scenarios, please refer to Figure 2 In one embodiment of the thin-walled workpiece milling machining control method of this application, the method includes:

[0095] Step S10: In response to the milling vibration suppression command for thin-walled workpieces, determine the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force, wherein the system transfer function is constructed from the system natural frequency and the system damping ratio;

[0096] The thin-walled workpiece milling control system can respond to the vibration suppression command during thin-walled workpiece milling and determine the frequency spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force. The system transfer function is constructed from the system natural frequency and the system damping ratio. Thin-walled workpiece milling refers to processing workpieces with relatively thin walls during the machining process. This type of machining has special requirements for process control and tool selection because thin-walled workpieces are prone to vibration, deformation, or even damage due to the accumulation of cutting force and heat.

[0097] Step S20: Based on the spindle speed control model that minimizes system vibration, determine the optimal spindle speed corresponding to the thin-walled workpiece spindle according to the spectrum function, system transfer function, system damping ratio and system damping frequency corresponding to the cutting force.

[0098] The spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force are determined. The system transfer function is constructed from the system natural frequency and the system damping ratio. Based on the spindle speed control model that minimizes system vibration, the optimal spindle speed corresponding to the spindle of the thin-walled workpiece is determined according to the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force.

[0099] Further steps in constructing the spindle speed control model include:

[0100] Step S201: Perform a fast Fourier transform on the cutting force to determine the frequency spectrum function corresponding to the cutting force;

[0101] Step S203: Determine the dynamic stiffness between the thin-walled workpiece and the tool and the equivalent mass of the thin-walled workpiece and the tool in the simplified two-degree-of-freedom vibration model. Based on the dynamic stiffness between the thin-walled workpiece and the tool and the equivalent mass of the thin-walled workpiece and the tool, determine the natural frequency of the system.

[0102] Step S205: Determine the system damping ratio. Calculate and determine the system damping frequency based on the system's natural frequency and the system damping ratio.

[0103] Step S207: Construct the system's transfer function based on the system's natural frequency and damping ratio;

[0104] Step S209: Based on the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force, construct a spindle speed control model to minimize system vibration.

[0105] Specifically, please refer to Figure 3Chatter is a significant problem in the milling of thin-walled workpieces, affecting machining accuracy and workpiece surface quality. Chatter is an unstable phenomenon caused by the interaction between cutting force and the natural frequencies of the workpiece and tool system. To address this issue, this application proposes a spindle speed control model to suppress chatter. By adjusting the spindle speed, the machining process is optimized, improving system stability and workpiece quality.

[0106] The simplified two-degree-of-freedom vibration model is expressed as follows:

[0107]

[0108] in, This represents the vibration displacement of the thin-walled workpiece at time t. The vibration velocity of the thin-walled workpiece is represented by x(t), the vibration acceleration of the thin-walled workpiece is represented by m, the equivalent mass of the thin-walled workpiece and the tool is represented by c, the damping coefficient is represented by k, the dynamic stiffness between the thin-walled workpiece and the tool is represented by F(t), and the spindle speed w affects the frequency component of F(t), thereby changing the vibration response of the system.

[0109] To optimize spindle speed and suppress chatter, a fast Fourier transform is first performed on the cutting force to obtain the corresponding spectrum function. The expression for the cutting force spectrum function is as follows:

[0110] F(w) = FFT(F(t)),

[0111] Where F(w) represents the frequency spectrum function corresponding to the cutting force;

[0112] Based on the dynamic stiffness between the thin-walled workpiece and the cutting tool, and the equivalent mass of the thin-walled workpiece and the cutting tool, the natural frequency of the system is determined, and the expression for the natural frequency of the system is:

[0113]

[0114] Among them, w n Indicates the system's natural frequency;

[0115] To avoid chatter, the spindle speed should be far from the system's resonant frequency. Damping effects should be considered, and the system's damping ratio ζ and damping frequency w should be adjusted accordingly. d Incorporating the spindle speed optimization process, the expression for the system damping frequency is:

[0116]

[0117] Among them, w d ζ represents the system damping frequency, and ζ represents the system damping ratio.

[0118] The spindle speed is optimized by considering the system's transfer function, which is expressed as follows:

[0119]

[0120] Where w represents the spindle speed;

[0121] The expression for the spindle speed control model that minimizes system vibration (the objective function that minimizes system flutter response) is as follows:

[0122] min w |H(w)F(w)|.

[0123] Based on the spindle speed control model constructed using the above formula, the optimal spindle speed corresponding to the spindle of a thin-walled workpiece can be determined.

[0124] Step S30: Respond to the system control parameter optimization command, determine the total cutting material volume and total energy consumption of the thin-walled workpiece, and construct a system control parameter optimization model to minimize the total energy consumption and maximize the total cutting material volume based on the total cutting material volume and the total energy consumption.

[0125] After determining the optimal spindle speed corresponding to the thin-walled workpiece spindle, the thin-walled workpiece milling control system can respond to the system control parameter optimization command to determine the total cutting material volume and total energy consumption of the thin-walled workpiece. Based on the total cutting material volume and the total energy consumption, a system control parameter optimization model is constructed to minimize the total energy consumption and maximize the total cutting material volume. The system control parameters include PID control parameters and feedforward control. The PID control parameters include proportional gain (Kp_Gain), integral gain (Ki_Gain), and derivative gain (Kd_Gain).

[0126] Furthermore, to improve system milling efficiency and reduce energy consumption, the Whale Optimization Algorithm (WOA) is employed to optimize the gain parameters in the control system, enabling the system to achieve optimal performance while meeting milling quality requirements. The optimization objective is to maximize the total cutting material volume of the thin-walled workpiece to ensure cutting efficiency, and to minimize the total energy waste of the thin-walled workpiece to improve system energy utilization.

[0127] The objective function for the total cut material volume of the thin-walled workpiece is:

[0128]

[0129] Where A(t) is the cutting area that changes with time, and M... total This indicates the total volume of material cut from a thin-walled workpiece;

[0130] The objective function for the total energy consumption of the thin-walled workpiece is:

[0131]

[0132] Among them, E waste S(t) represents the total energy consumption of the thin-walled workpiece, and S(t) is the spindle speed that varies with time. op The optimal speed is 2000 r / min. When the spindle speed exceeds this optimal speed, it will lead to a waste of power resources.

[0133] The objective function comprehensively considers the total cutting material volume and the total energy consumption of the thin-walled workpiece. Its goal is to maximize the total cutting material volume and minimize the total energy consumption of the thin-walled workpiece. The optimal combination of system control parameters is found by solving the optimal solution of the system control parameter optimization model. The expression for the system control parameter optimization model is as follows:

[0134] f(x) = M total +E waste .

[0135] Step S40: Solve the system control parameter optimization model based on the preset whale optimization algorithm to determine the optimal system control parameters, wherein the system control parameters represent the system control parameter combination constructed by proportional gain, integral gain, derivative gain and feedforward control;

[0136] After constructing a system control parameter optimization model to minimize the total energy consumption and maximize the total volume of cut material, the system control parameter optimization model is solved based on a preset whale optimization algorithm to determine the optimal system control parameters. The system control parameters represent a combination of system control parameters constructed from proportional gain, integral gain, derivative gain, and feedforward control.

[0137] The Whale Optimization Algorithm (WOA) is used to optimize the system control parameters, namely proportional gain (Kp_Gain), integral gain (Ki_Gain), derivative gain (Kd_Gain), and feedforward control.

[0138] The Whale Optimization Algorithm (WOA) mimics the hunting behavior of whales, continuously adjusting its position to approach prey and ultimately find the optimal solution. This includes three hunting behaviors: first, surrounding the prey: continuously adjusting its position to surround the optimal solution; second, spiraling position updates: simulating the whale's spiral ascent around the prey; and third, random prey search: increasing diversity through random searches to avoid local optima. Finally, a convergence check is performed, stopping iteration when the maximum number of iterations is reached or the fitness value no longer changes significantly. The algorithm ultimately outputs the optimal combination of system control parameters, improving the efficiency and stability of the cutting process, reducing energy consumption, and thus lowering production costs and environmental impact.

[0139] Specifically, the steps of solving the system control parameter optimization model based on the preset whale optimization algorithm to determine the optimal system control parameters include:

[0140] Step S401: Call the preset whale optimization algorithm, solve the system control parameter optimization model based on the whale optimization algorithm, and determine the initial parameters of the whale optimization algorithm, including the population size and the number of iterations;

[0141] Step S403: Randomly initialize the whale population according to the population size to generate initial whale individuals, wherein the whale individual is characterized by a combination of system control parameters constructed by proportional gain, integral gain, differential gain and feedforward control.

[0142] Step S405: For each iteration step, calculate the fitness objective function value for each individual whale, and update the position and velocity of each individual whale based on the fitness objective function value;

[0143] Step S407: During the algorithm's iteration process, select the whale individual with the optimal fitness objective function value as the optimal combination of system control parameters;

[0144] Step S409: When the preset number of iterations is reached or the convergence condition is met, the optimization process ends, and the optimal combination of system control parameters constructed by proportional gain, integral gain, derivative gain and feedforward control is obtained.

[0145] Step S50: Based on the optimal spindle speed and optimal system control parameters corresponding to the thin-walled workpiece spindle, control the thin-walled workpiece to be milled to complete the milling control of the thin-walled workpiece.

[0146] After determining the optimal system control parameters, the thin-walled workpiece to be milled is controlled to perform machining based on the optimal spindle speed corresponding to the thin-walled workpiece spindle and the optimal system control parameters, so as to complete the machining control of the thin-walled workpiece milling.

[0147] In some embodiments, please refer to Figure 4 , Figure 4 A spindle speed control system based on a PID controller and a feedforward control module is demonstrated, which is mainly used to adjust the spindle speed of the tool. Figure 4 The SetSpeed ​​module indicates that the target rotational speed is set to 2000 r / min. This target value is subtracted from the actual feed rate signal (feed_rate_signal) to obtain an error signal. The error signal is adjusted by a PID controller. The PID controller parameters include proportional gain (Kp_Gain), integral gain (Ki_Gain), and derivative gain (Kd_Gain). The proportional gain (Kp_Gain) is used to adjust the proportional part of the error signal, the integral gain (Ki_Gain) is adjusted by an integrator (1 / s) to adjust the integral part of the error signal, and the derivative gain (Kd_Gain) is adjusted by a differentiator (Δu / Δt) to adjust the derivative part of the error signal. After each gain module performs corresponding calculations on the error signal, the results are added together to generate a control signal.

[0148] The feedforward control module directly incorporates the actual feed rate signal into the control signal by multiplying it by the feedforward gain, thereby compensating for the dynamic error of the system and improving control accuracy.

[0149] The FOC (Field Oriented Control) module represents a first-order transfer function (1 / (0.1s+1)) used to simulate the dynamic response of an actual motor. This module receives the control signal output by the PID controller and generates the actual spindle speed. The transfer function 1 / (0.1s+1) represents a first-order system with a time constant of 0.1 seconds.

[0150] The control signal (control_s) is output from the FOC module, representing the actual spindle speed signal. This signal is used in the feedback control system, compared with the setpoint, to form a closed-loop control system, ensuring the spindle speed remains stable near the desired value. Through error signal calculation, PID control, feedforward control, motor dynamic response, and feedback loop, this Simulink model achieves precise and efficient speed control, ensuring the spindle speed remains stable at the setpoint of 2000 r / min. The PID controller adjusts the error signal, feedforward control compensates for dynamic errors, and the FOC module simulates the motor's dynamic response, ultimately achieving stable system control.

[0151] In some embodiments, an initial thin-wall thickness signal varying over time is first generated using a Simulink model, with the formula Wall = 1 + 0.5 × sin(2 × p). i ×t), where the tool width is set to a constant of 10mm.

[0152] During the simulation, the feed rate was fixed at 200 mm / min. The relationship between the depth of cut and the tool spindle speed was calculated, with the optimal depth of cut set at 1.5 mm. The cutting performance was optimal when the spindle speed was greater than or equal to 2000 rpm; when the spindle speed was less than 2000 rpm, the cutting performance decreased proportionally. The thickness of the thin wall after cutting was calculated using the cutting performance. Through cumulative calculation, the total volume of material removed and the total energy waste were obtained.

[0153] Please see Figures 5 to 7 ,in, Figure 5 The curves showing the initial wall thickness of a thin-walled workpiece changing over time under feedforward control combined with PID control are displayed. Figure 6 The curve of the spindle speed of a thin-walled workpiece changing over time is shown, which combines feedforward control with PID control. Figure 7 The curve showing the final wall thickness of a thin-walled workpiece over time is displayed using feedforward control combined with PID control. From... Figures 5 to 7 As can be seen from the data, after adding feedforward control, the spindle speed response is faster, the cutting process is more stable, and the final wall thickness change is smooth.

[0154] Please see Figures 8 to 10 , Figure 8 This is the curve showing the initial wall thickness changing over time under simple PID control. Figure 9 This is a curve showing the spindle speed as a function of time under simple PID control. Figure 10 The curve showing the final wall thickness over time under simple PID control shows that the spindle speed fluctuates significantly over time, the cutting process is not stable enough, and the final wall thickness change is not as smooth as when feedforward control is added.

[0155] By comparing the simulation results of the two control strategies, it can be seen that the PID system with feedforward control exhibits better performance in improving cutting efficiency and reducing energy waste. Specifically, the simulation results show a significant increase in the total removed material volume and a reduction in total energy waste, thus verifying the effectiveness of the optimized control parameters.

[0156] As can be seen from the above embodiments, compared with the prior art, the traditional thin-walled workpiece milling method in this application usually requires multiple trial cuts and adjustments on actual machine tools, which not only occupies a lot of machine tool resources, but also, due to the complex shape and material properties of thin-walled workpieces, the success rate of processing is often not high, requiring repeated adjustments and trials, which consumes a lot of time and human resources. This application has the following beneficial effects, including but not limited to:

[0157] Firstly, this application constructs a milling framework for thin-walled workpieces based on digital twins. This framework utilizes virtual-real combination technology to ensure the efficiency and quality of thin-walled workpieces in actual processing scenarios.

[0158] Secondly, this application designs a spindle speed control model for thin-walled workpieces. This model effectively reduces chatter and deformation, improves machining accuracy, and enhances overall machining efficiency and product qualification rate by adjusting the dynamic control method of spindle speed.

[0159] Third, this application constructs a method for solving the system control parameter optimization model based on a preset whale optimization algorithm. This method is based on the powerful global search capability and fast convergence characteristics of the whale optimization algorithm, which can automatically optimize PID controller parameters and feedforward control, thereby improving the control accuracy and response speed of the system.

[0160] Fourth, the method of solving the system control parameter optimization model based on the preset whale optimization algorithm in this application can effectively improve cutting efficiency and reduce energy waste by determining the optimal combination of PID controller parameters and feedforward control.

[0161] Please see Figure 11 A control device for milling thin-walled workpieces, provided to meet one of the purposes of this application, includes a data acquisition module 1100, an optimal spindle speed determination module 1200, a parameter optimization model construction module 1300, an optimal parameter combination determination module 1400, and a milling control module 1500. The data acquisition module 1100 is configured to determine the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force in response to a thin-walled workpiece milling vibration suppression command, wherein the system transfer function is constructed from the system natural frequency and the system damping ratio. The optimal spindle speed determination module 1200 is configured to determine the optimal spindle speed corresponding to the thin-walled workpiece spindle based on the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force, using a spindle speed control model that minimizes system vibration. The parameter optimization model construction module 1300 is configured to determine the total cutting material volume and total energy consumption of the thin-walled workpiece in response to a system control parameter optimization command. Based on the total volume of material being cut and the total energy consumption, a system control parameter optimization model is constructed to minimize the total energy consumption and maximize the total volume of material being cut. An optimal parameter combination determination module 1400 is configured to solve the system control parameter optimization model based on a preset whale optimization algorithm to determine the optimal system control parameters, wherein the system control parameters represent a combination of system control parameters constructed from proportional gain, integral gain, derivative gain, and feedforward control. A milling machining control module 1500 is configured to control the thin-walled workpiece to be milled based on the optimal spindle speed corresponding to the thin-walled workpiece spindle and the optimal system control parameters, thereby completing the milling control of the thin-walled workpiece.

[0162] Based on any embodiment of this application, please refer to Figure 12Another embodiment of this application also provides an electronic device, which can be implemented by a computer device, such as... Figure 12 The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store a sequence of control information. When the computer-readable instructions are executed by the processor, the processor can implement a thin-walled workpiece milling control method. The processor of the computer device provides computing and control capabilities, supporting the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the thin-walled workpiece milling control method of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0163] In this embodiment, the processor is used to execute... Figure 11 The system contains the specific functions of each module and its sub-modules, and the memory stores the program code and various data required to execute these modules or sub-modules. A network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules / sub-modules in the thin-walled workpiece milling control device of this application, and the server can call the server's program code and data to execute the functions of all sub-modules.

[0164] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the thin-walled workpiece milling control method described in any embodiment of this application.

[0165] This application also provides a computer program product, including a computer program / instructions that, when executed by one or more processors, implement the steps of the thin-walled workpiece milling control method described in any embodiment of this application.

[0166] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0167] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

[0168] In summary, this application constructs a method for solving the system control parameter optimization model based on a preset whale optimization algorithm. This method leverages the powerful global search capability and fast convergence characteristics of the whale optimization algorithm, which can automatically optimize PID controller parameters and feedforward control, thereby improving the system's control accuracy and response speed. Furthermore, this method for solving the system control parameter optimization model based on the preset whale optimization algorithm can effectively improve cutting efficiency and reduce energy waste by determining the optimal combination of PID controller parameters and feedforward control.

Claims

1. A method for controlling the milling process of thin-walled workpieces, characterized in that, include: In response to the vibration suppression command during thin-walled workpiece milling, the spectral function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force are determined. The system transfer function is constructed from the system's natural frequencies and the system damping ratio. The expression for the system damping frequency is as follows: ， in, Indicates the system damping frequency. Indicates the system damping ratio; The expression for the system transfer function is: ， in, Indicates the spindle speed. This indicates the equivalent mass of thin-walled workpieces and cutting tools. Indicates the system's natural frequency; Based on the spindle speed control model that minimizes system vibration, the optimal spindle speed corresponding to the thin-walled workpiece spindle is determined according to the spectrum function corresponding to the cutting force, the system transfer function, the system damping ratio, and the system damping frequency. The expression for the spindle speed control model that minimizes system vibration is as follows: , This represents the spectrum function corresponding to the cutting force. In response to system control parameter optimization commands, the total cutting material volume and total energy consumption of the thin-walled workpiece are determined. Based on the total cutting material volume and total energy consumption, a system control parameter optimization model is constructed to minimize the total energy consumption and maximize the total cutting material volume, which includes: The objective function for the total cut material volume of the thin-walled workpiece is: ， in, It is the cutting area that changes over time. This indicates the total volume of material cut from a thin-walled workpiece; The objective function for the total energy consumption of the thin-walled workpiece is: ， in, This indicates the total energy consumption of thin-walled workpieces. It is the spindle speed that varies with time. This is the optimal speed. When the spindle speed exceeds this optimal speed, it will lead to a waste of power resources. The expression for the system control parameter optimization model is: ； The system control parameter optimization model is solved based on the preset whale optimization algorithm to determine the optimal system control parameters, wherein the system control parameters represent the system control parameter combination constructed by proportional gain, integral gain, derivative gain and feedforward control; Based on the optimal spindle speed and optimal system control parameters corresponding to the thin-walled workpiece spindle, the thin-walled workpiece to be milled is controlled to perform machining, thereby completing the machining control of thin-walled workpiece milling.

2. The method for controlling the milling of thin-walled workpieces according to claim 1, characterized in that, The steps for constructing a spindle speed control model include: Perform a fast Fourier transform on the cutting force to determine the corresponding spectral function of the cutting force; The dynamic stiffness between the thin-walled workpiece and the cutting tool, as well as the equivalent mass of the thin-walled workpiece and the cutting tool, are determined in a simplified two-degree-of-freedom vibration model. Based on the dynamic stiffness between the thin-walled workpiece and the cutting tool, as well as the equivalent mass of the thin-walled workpiece and the cutting tool, the natural frequency of the system is determined. Determine the system damping ratio, and calculate the system damping frequency based on the system's natural frequency and the system damping ratio; The system's transfer function is constructed based on the system's natural frequency and damping ratio. Based on the spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force, a spindle speed control model is constructed to minimize system vibration.

3. The method for controlling the milling of thin-walled workpieces according to claim 2, characterized in that, The simplified two-degree-of-freedom vibration model is expressed as follows: , in, Representing thin-walled workpieces Vibration displacement at time t, The vibration velocity representing the thin-walled workpiece. The vibration acceleration representing the thin-walled workpiece. This indicates the equivalent mass of thin-walled workpieces and cutting tools. It is the damping coefficient. It refers to the dynamic stiffness between the thin-walled workpiece and the cutting tool. This indicates the cutting force.

4. The method for controlling the milling of thin-walled workpieces according to claim 3, characterized in that, The expression for the frequency spectrum function corresponding to the cutting force is: ， in, This represents the spectrum function corresponding to the cutting force. The expression for the system's natural frequency is: ， in, This indicates the system's inherent frequency.

5. The method for controlling the milling of thin-walled workpieces according to claim 1, characterized in that, The steps for solving the system control parameter optimization model based on the preset whale optimization algorithm to determine the optimal system control parameters include: The preset whale optimization algorithm is invoked, and the system control parameter optimization model is solved based on the whale optimization algorithm to determine the initial parameters of the whale optimization algorithm, including the population size and the number of iterations; The whale population is randomly initialized according to the population size to generate initial whale individuals, wherein the whale individual is characterized by a combination of system control parameters constructed by proportional gain, integral gain, differential gain and feedforward control. For each iteration step, the fitness objective function value of each individual whale is calculated, and the position and velocity of each individual whale are updated based on the fitness objective function value; During the algorithm's iteration process, the whale individual with the optimal fitness objective function value is selected as the optimal combination of system control parameters; When the preset number of iterations is reached or the convergence condition is met, the optimization process ends, and the optimal combination of system control parameters constructed by proportional gain, integral gain, derivative gain and feedforward control is obtained.

6. The method for controlling the milling of thin-walled workpieces according to claim 1, characterized in that, The optimal speed is 2000 r / min.

7. A control device for milling thin-walled workpieces, characterized in that, include: The data acquisition module is configured to respond to vibration suppression commands during thin-walled workpiece milling, determining the frequency spectrum function, system transfer function, system damping ratio, and system damping frequency corresponding to the cutting force. The system transfer function is constructed from the system's natural frequency and system damping ratio, and the expression for the system damping frequency is: ， in, Indicates the system damping frequency. Indicates the system damping ratio; The expression for the system transfer function is: ， in, Indicates the spindle speed. This indicates the equivalent mass of thin-walled workpieces and cutting tools. Indicates the system's natural frequency; The optimal spindle speed determination module is configured to determine the optimal spindle speed corresponding to the thin-walled workpiece spindle based on a spindle speed control model that minimizes system vibration, according to the spectrum function corresponding to the cutting force, the system transfer function, the system damping ratio, and the system damping frequency. The expression for the spindle speed control model that minimizes system vibration is as follows: , This represents the spectrum function corresponding to the cutting force. The parameter optimization model construction module is configured to respond to system control parameter optimization commands, determine the total cutting material volume and total energy consumption of the thin-walled workpiece, and construct a system control parameter optimization model based on the total cutting material volume and the total energy consumption to minimize the total energy consumption and maximize the total cutting material volume. This model includes: The objective function for the total cut material volume of the thin-walled workpiece is: ， in, It is the cutting area that changes over time. This indicates the total volume of material cut from a thin-walled workpiece; The objective function for the total energy consumption of the thin-walled workpiece is: ， in, This indicates the total energy consumption of thin-walled workpieces. It is the spindle speed that varies with time. This is the optimal speed. When the spindle speed exceeds this optimal speed, it will lead to a waste of power resources. The expression for the system control parameter optimization model is: ； The optimal parameter combination determination module is configured to solve the system control parameter optimization model based on a preset whale optimization algorithm to determine the optimal system control parameters, wherein the system control parameters represent the system control parameter combination constructed by proportional gain, integral gain, derivative gain and feedforward control; The milling control module is configured to control the thin-walled workpiece to be milled based on the optimal spindle speed and optimal system control parameters corresponding to the spindle of the thin-walled workpiece, so as to complete the milling control of the thin-walled workpiece.

8. An electronic device comprising a central processing unit and a memory, characterized in that, The central processing unit is used to invoke and run a computer program stored in the memory to perform the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores, in the form of computer-readable instructions, a computer program implemented according to any one of claims 1 to 6, which, when invoked by a computer, executes the steps included in the corresponding method.

Images