Thin-wall workpiece milling control method, device, equipment and medium

Abstract

The invention relates to a thin-wall workpiece milling control method, device and equipment and a medium, and the method comprises the steps that 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, and the system control parameters represent a system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control; and based on the optimal main shaft rotating speed corresponding to the main shaft of the thin-wall workpiece and the optimal system control parameters, the thin-wall workpiece to be milled is controlled to be machined, so that machining control over milling of the thin-wall workpiece is completed. According to the invention, PID controller parameters and feedforward control can be automatically optimized, and the control precision and response speed of the system are improved.

Description

Technical Field

[0001] The present application relates to the field of parts processing, and in particular to a thin-wall workpiece milling control method, a corresponding device, an electronic device and a computer-readable storage medium. Background Art

[0002] Thin-walled workpieces are characterized by high strength and light weight, and are widely used in the aerospace field, such as aircraft brackets, fuselages and other key parts. However, milling of thin-walled workpieces is prone to chatter. When the cutting force between the tool and the thin-walled workpiece reaches a certain critical value, the system will produce unstable self-excited vibrations, i.e. chatter. This phenomenon will not only increase the roughness of the machined surface, but also cause deviations in the size of the thin-walled workpiece and a decrease in machining accuracy. There are many factors that cause chatter, including the rigidity of the tool, the clamping method of the workpiece, the selection of cutting parameters, the dynamic characteristics of the machine tool, and the stability of the machining environment.

[0003] Traditional thin-wall workpiece milling methods usually require multiple trial cuts and adjustments on actual machine tools to determine the optimal processing parameters and process flow. This method not only takes up a lot of machine tool resources, but also due to the complex shape and material properties of thin-walled workpieces, the processing success rate is often not high. Repeated adjustments and tests are required, which consumes a lot of time and human resources. At the same time, the processing process is easily affected by vibration, workpiece deformation and other problems, further increasing the difficulty and risk of processing.

[0004] To sum up, the traditional thin-walled workpiece milling method in the prior art usually requires multiple trial cutting and adjustments on the actual machine tool, which not only takes up a large amount of machine tool resources, but also due to the complex shape and material properties of the thin-walled workpiece, the processing success rate is often not high, and repeated adjustments and tests are required, which consumes a lot of time and human resources. In order to solve this problem, the applicant has made corresponding explorations. Summary of the invention

[0005] The purpose of the present application is to solve the above-mentioned problems and to provide a thin-wall workpiece milling control method, a corresponding device, an electronic device and a computer-readable storage medium.

[0006] In order to meet the various objectives of this application, this application adopts the following technical solutions:

[0007] A thin-wall workpiece milling control method proposed to meet one of the purposes of this application includes:

[0008] In response to a thin-wall workpiece milling vibration suppression instruction, a frequency spectrum function, a system transfer function, a system damping ratio and a system damping frequency corresponding to the cutting force are determined, wherein the system transfer function is constructed by the system natural frequency and the system damping ratio;

[0009] Based on the spindle speed control model for minimizing 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;

[0010] In response to a system control parameter optimization instruction, determining a total cutting material volume and a total energy consumption of the thin-walled workpiece, and constructing a system control parameter optimization model for minimizing the total energy consumption and maximizing the total cutting material volume based on the total cutting material volume and the total energy consumption;

[0011] Solving 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 by proportional gain, integral gain, differential gain and feedforward control;

[0012] Based on the optimal spindle speed corresponding to the thin-walled workpiece spindle and the optimal system control parameters, the thin-walled workpiece to be milled is controlled to be processed to complete the processing control of the thin-walled workpiece milling.

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

[0014] Performing fast Fourier transform on the cutting force to determine a frequency spectrum function corresponding to the cutting force;

[0015] 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 a simplified two-degree-of-freedom vibration model, and determine the natural frequency of the system 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;

[0016] Determine the system damping ratio, and calculate and determine the system damping frequency according to the system natural frequency and the system damping ratio;

[0017] Based on the natural frequency of the system and the damping ratio of the system, a transfer function of the system is constructed;

[0018] Based on the frequency 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 expression of the simplified two-degree-of-freedom vibration model is:

[0020]

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

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

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

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

[0025] The expression of the natural frequency of the system is:

[0026]

[0027] Among them, w n represents the natural frequency of the system;

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

[0029]

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

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

[0032]

[0033] Wherein, w represents the spindle speed;

[0034] The expression of the spindle speed control model to minimize system vibration is:

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

[0036] Optionally, the step of determining the total cutting material volume and the 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 comprises:

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

[0038]

[0039] Where A(t) is the cutting area varying with time, M total Indicates the total volume of cut material for thin-walled workpieces;

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

[0041]

[0042] Among them, E waste represents the total energy consumption of the thin-walled workpiece, S(t) is the time-varying spindle speed, S op is the optimal speed. When the spindle speed is greater than the optimal speed, it will lead to a waste of power resources.

[0043] The expression of 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] Calling a preset whale optimization algorithm, solving the system control parameter optimization model based on the whale optimization algorithm, and determining initial parameters of the whale optimization algorithm, wherein the initial parameters include a population size and a number of iterations;

[0047] Randomly initializing a whale population according to the population size to generate initial whale individuals, wherein the whale individuals represent a system control parameter combination 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 speed of each individual whale are updated according to the fitness objective function value;

[0049] In the iterative process of the algorithm, the whale individual with the best fitness objective function value is selected as the optimal system control parameter combination;

[0050] When the preset number of iterations is reached or the convergence condition is met, the optimization process is terminated to obtain the optimal system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control.

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

[0052] A thin-wall workpiece milling control device provided for another purpose of the present application includes:

[0053] A data acquisition module is configured to respond to a vibration suppression instruction for milling a thin-walled workpiece and determine a frequency spectrum function, a system transfer function, a system damping ratio, and a system damping frequency corresponding to the cutting force, wherein the system transfer function is constructed by the system natural frequency and the system damping ratio;

[0054] an optimal speed determination module, configured to determine an optimal spindle speed corresponding to a thin-walled workpiece spindle according to a spectrum function corresponding to the cutting force, a system transfer function, a system damping ratio, and a system damping frequency based on a spindle speed control model for minimizing system vibration;

[0055] a parameter optimization model building module, configured to respond to a system control parameter optimization instruction, determine a total cutting material volume and a total energy consumption of the thin-walled workpiece, and build 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] An 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 a system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control;

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

[0058] An electronic device provided to meet another purpose of the present application includes a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the thin-wall workpiece milling control method described in the present application.

[0059] A computer-readable storage medium is provided to meet another purpose of the present application, which stores a computer program implemented according to the thin-walled workpiece milling control method in the form of computer-readable instructions. When the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

[0060] Compared with the prior art, the present application aims at the problem that the conventional milling method of thin-walled workpiece in the prior art usually requires multiple trial cutting and adjustment on the actual machine tool, which not only occupies a large amount of machine tool resources, but also due to the complex shape and material properties of the thin-walled workpiece, the processing success rate is often not high, and repeated adjustment and testing are required, which consumes a lot of time and human resources. The present application includes but is not limited to the following beneficial effects:

[0061] First, this application constructs a thin-walled workpiece milling processing framework based on digital twins, which uses 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 improves overall machining efficiency and product qualification rate by adjusting the dynamic control method of the 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 whale optimization algorithm, which has powerful global search capabilities and fast convergence characteristics, and can automatically optimize PID controller parameters and feedforward control to improve the control accuracy and response speed of the system.

[0064] Fourthly, the present application is a method for solving the system control parameter optimization model based on a preset whale optimization algorithm, which can effectively improve cutting efficiency and reduce energy waste by determining the optimal combination of PID controller parameters and feedforward control. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0067] Figure 2 This is a flow chart of a thin-wall workpiece milling control method in an embodiment of the present application;

[0068] Figure 3 A schematic diagram of constructing a spindle speed control model in an embodiment of the present application;

[0069] Figure 4 This is a flow chart of a spindle speed control system based on a PID controller and a feedforward control module in an embodiment of the present application;

[0070] Figure 5 A schematic diagram of a curve showing the change of the initial wall thickness of a thin-walled workpiece over time in the feedforward control combined with PID control in an embodiment of the present application;

[0071] Figure 6 This is a schematic diagram of a curve showing the change of the spindle speed of a thin-walled workpiece with time under feedforward control combined with PID control in an embodiment of the present application;

[0072] Figure 7 A schematic diagram of a curve showing a curve of a final wall thickness of a thin-walled workpiece changing over time by feedforward control combined with PID control in an embodiment of the present application;

[0073] Figure 8 This is a schematic diagram of a curve showing the change of initial wall thickness over time under simple PID control in an embodiment of the present application;

[0074] Fig. 9 This is a schematic diagram of a curve showing the change of the spindle speed over time under pure PID control in an embodiment of the present application;

[0075] Fig.10 This is a schematic diagram of a curve showing the final wall thickness changing with time under pure PID control in an embodiment of the present application;

[0076] Fig.11 This is a principle block diagram of a thin-wall workpiece milling control device in an embodiment of the present application;

[0077] Fig.12 It is a schematic diagram of the structure of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0078] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0079] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the 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 refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit 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 (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0081] It will be understood by those skilled in the art that the "client", "terminal" and "terminal device" used herein include both devices with wireless signal receivers, which are devices with only wireless signal receivers without transmission capabilities, and devices with receiving and transmitting hardware, which are devices with receiving and transmitting hardware capable of two-way communication on a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers, tablet computers, which have single-line displays or multi-line displays or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service, personal communication system), which can combine voice, data processing, fax and / or data communication capabilities; PDA (Personal Digital Assistant, personal digital assistant), which may include a radio frequency receiver, pager, Internet / intranet access, web browser, notepad, calendar and / or GPS (Global Positioning System, global positioning system) receiver; conventional laptop and / or palmtop computers or other devices, which have and / or include a conventional laptop and / or palmtop computer or other device with and / or including a radio frequency receiver. The "client", "terminal" and "terminal device" used herein may be portable, transportable, installed in a vehicle (air, sea and / or land), or suitable for and / or configured to run locally, and / or in a distributed form, at any other location on the earth and / or in space. The "client", "terminal" and "terminal device" used herein may also be a communication terminal, an Internet terminal, a music / video playing terminal, for example, a PDA, a MID (Mobile Internet Device) and / or a mobile phone with a music / video playing function, or a smart TV, a set-top box and other devices.

[0082] The hardware referred to by the names such as "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 principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. The computer program is stored in its memory, and the central processing unit calls the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0083] It should be pointed out that the concept of "server" referred to in this application can also be extended to the case of server clusters. According to the network deployment principle understood by those skilled in the art, the servers should be logically divided. In physical space, these servers can be independent of each other but can be called through interfaces, or integrated into a physical computer or a set of computer clusters. Those skilled in the art should understand this flexibility, and should not use it to restrict the implementation of the network deployment method of this application.

[0084] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for access.

[0085] The neural network models referenced or may be referenced in this application, unless expressly specified, can be deployed on a remote server and remotely called on the client, or can be deployed and directly called on a client with sufficient device capabilities. In some embodiments, when it runs on the client, its corresponding intelligence can be obtained through transfer learning to reduce the requirements for the client's hardware operating resources and avoid excessive occupation of the client's hardware operating resources.

[0086] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as it is suitable for being called by the technical solution of this application.

[0087] Those skilled in the art should be aware that, although the various methods of the present application are described based on the same concept and thus present commonality to each other, unless otherwise specified, these methods can be independently executed. Similarly, for each embodiment disclosed in the present application, they are all proposed based on the same inventive concept, therefore, concepts with the same expression, and concepts that are appropriately changed for convenience despite different expressions, should be understood as equivalent.

[0088] Unless the mutually exclusive relationship between the embodiments to be disclosed in this application is explicitly stated, the relevant technical features involved in each embodiment can be cross-combined to flexibly construct a new embodiment, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or solve certain deficiencies in the prior art. Those skilled in the art should be aware of this flexibility.

[0089] See also Figure 1The thin-walled workpiece milling control method of the present application can be executed by a thin-walled workpiece milling control system based on digital twin. The thin-walled workpiece milling control system combines digital twin technology, Internet of Things technology, electrical control, dynamic model and other technologies to reduce the chatter phenomenon in the thin-walled workpiece milling process, thereby improving the processing quality. The thin-walled workpiece milling control system includes a physical layer part, a virtual layer part, 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-wall processing system in real time, simulate the motion process of thin-wall processing, and 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 processing scenario through the mutual cooperation of the mechanical system, electrical system, and control system. At the same time, the data acquisition system uses torque sensors, pressure sensors, displacement sensors, and temperature sensors to collect various parameters in the milling process in real time, and feeds the data back to the control system and the virtual model to form a closed-loop control.

[0091] In some embodiments, the virtual layer partially displays a virtual thin-wall processing scene, and a complete thin-wall processing scene is simulated through the joint action of virtual components such as virtual drivers, actuators, and virtual tools. This virtual processing scene can truly reproduce various situations in actual processing, thereby providing accurate data and feedback for actual processing.

[0092] In some embodiments, the dynamic model continuously adjusts and optimizes its parameters through data provided by the physical and virtual models, thereby reducing chatter during the milling process and improving the accuracy and reliability of the system.

[0093] In some embodiments, the PID controller part receives data from the physical layer model and the virtual layer model, and performs 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, the thin-wall workpiece milling control method of the present application includes:

[0095] Step S10, responding to the thin-wall workpiece milling vibration suppression instruction, determining the frequency spectrum function, system transfer function, system damping ratio and system damping frequency corresponding to the cutting force, wherein the system transfer function is constructed by the system natural frequency and the system damping ratio;

[0096] The thin-walled workpiece milling control system can respond to the thin-walled workpiece milling vibration suppression instruction, determine the spectral function, system transfer function, system damping ratio and system damping frequency corresponding to the cutting force, wherein the system transfer function is constructed by the system natural frequency and the system damping ratio, and the thin-walled workpiece milling refers to the processing of thinner workpiece wall thickness during the processing. This type of processing has special requirements for process control and tool selection, because thin-walled workpieces are prone to vibration, deformation and even damage due to cutting force and heat accumulation.

[0097] Step S20, determining the optimal spindle speed corresponding to the thin-walled workpiece spindle based on the spindle speed control model for minimizing 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;

[0098] 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 by the system natural frequency and the system damping ratio, and then determine the optimal spindle speed corresponding to the thin-walled workpiece spindle based on the spindle speed control model for minimizing system vibration according to the spectrum function, system transfer function, system damping ratio and system damping frequency corresponding to the cutting force;

[0099] Furthermore, the steps of constructing the spindle speed control model include:

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

[0101] Step S203, determining 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, and determining the natural frequency of the system 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;

[0102] Step S205, determining the system damping ratio, and calculating and determining the system damping frequency according to the system natural frequency and the system damping ratio;

[0103] Step S207: constructing a transfer function of the system based on the natural frequency of the system and the damping ratio of the system;

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

[0105] Specifically, see Figure 3In the milling of thin-walled workpieces, chatter is a significant problem that affects the machining accuracy and workpiece surface quality. Chatter is an unstable phenomenon caused by the interaction between the cutting force and the system natural frequency of the workpiece and tool system. In order to solve this problem, this application proposes a spindle speed control model to suppress chatter. By adjusting the spindle speed, the machining process is optimized, and the system stability and workpiece quality are improved.

[0106] The expression of the simplified two-degree-of-freedom vibration model is:

[0107]

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

[0109] In order to optimize the spindle speed to suppress chatter, the cutting force is first subjected to a fast Fourier transform to obtain the corresponding spectrum function of the cutting force. The expression of the corresponding spectrum function of the cutting force is:

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

[0111] Among them, 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 tool and the equivalent mass of the thin-walled workpiece and the tool, the natural frequency of the system is determined. The expression of the natural frequency of the system is:

[0113]

[0114] Among them, w n represents the natural frequency of the system;

[0115] In order to avoid chatter, the spindle speed should be far away from the resonant frequency of the system, and the damping effect should be considered. The damping ratio ζ and damping frequency w of the system should be d Incorporating the spindle speed optimization process, the expression for the system damping frequency is:

[0116]

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

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

[0119]

[0120] Wherein, w represents the spindle speed;

[0121] The expression of the spindle speed control model to minimize the system vibration (the objective function to minimize the system chatter response) is:

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

[0123] The spindle speed control model constructed based on the above formula can determine the optimal spindle speed corresponding to the thin-walled workpiece spindle.

[0124] Step S30, responding to the system control parameter optimization instruction, determining the total cutting material volume and the total energy consumption of the thin-walled workpiece, and constructing 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 spindle of the thin-walled workpiece, the thin-walled workpiece milling control system can respond to the system control parameter optimization instruction, determine the total cutting material volume and the 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, wherein the system control parameters include PID control parameters and feedforward control, and the PID control parameters include proportional gain (Kp_Gain), integral gain (Ki_Gain), and differential gain (Kd_Gain);

[0126] Furthermore, in order to improve the milling efficiency of the system and reduce energy consumption, the whale optimization algorithm (WOA) is used to optimize the gain parameters in the control system so that the system can achieve optimal performance while meeting the milling quality. The optimization goal is to maximize the total cutting material volume of the thin-walled workpiece to ensure cutting efficiency, and minimize the total energy consumption waste of the thin-walled workpiece to improve the energy utilization of the system.

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

[0128]

[0129] Where A(t) is the cutting area varying with time, M total Indicates the total volume of cut material for thin-walled workpieces;

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

[0131]

[0132] Among them, E waste represents the total energy consumption of the thin-walled workpiece, S(t) is the time-varying spindle speed, S op It is the optimal speed. When the spindle speed is greater than the optimal speed, it will lead to waste of power resources. The optimal speed may be 2000r / min.

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

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

[0135] Step S40, solving the system control parameter optimization model based on a preset whale optimization algorithm to determine the optimal system control parameter, wherein the system control parameter represents a system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control;

[0136] After constructing a system control parameter optimization model to minimize the total energy consumption and maximize the total cutting material volume, solving 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 by proportional gain, integral gain, differential 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), differential gain (Kd_Gain) and feedforward control (feedforward).

[0138] The Whale Optimization Algorithm (WOA) imitates the hunting behavior of whales, constantly adjusting its position to approach its prey, and ultimately finding the optimal solution, which includes three hunting behaviors: first, surrounding the prey: surrounding the optimal solution by constantly adjusting the position; second, spirally updating the position: simulating the behavior of whales spiraling around their prey; third, randomly searching for prey: increasing diversity through random search to avoid local optimal solutions; finally, performing a convergence judgment and stopping the iteration when the maximum number of iterations is reached or the fitness value no longer changes significantly, and ultimately outputting the optimal combination of system control parameters to improve the efficiency and stability of the cutting process, reduce energy consumption, and thereby reduce production costs and environmental impact.

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

[0140] Step S401, calling a preset whale optimization algorithm, solving the system control parameter optimization model based on the whale optimization algorithm, and determining the initial parameters of the whale optimization algorithm, wherein the initial parameters include a population size and a number of iterations;

[0141] Step S403: randomly initializing the whale population according to the population size to generate initial whale individuals, wherein the whale individuals represent a system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control;

[0142] Step S405: For each iteration step, the fitness objective function value of each individual whale is calculated, and the position and speed of each individual whale are updated according to the fitness objective function value;

[0143] Step S407: During the algorithm iteration process, the whale individual with the best fitness objective function value is selected as the optimal system control parameter combination;

[0144] Step S409: When the preset number of iterations is reached or the convergence condition is met, the optimization process is terminated to obtain the optimal system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control.

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

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

[0147] In some embodiments, see Figure 4 , Figure 4 A spindle speed control system based on PID controller and feedforward control module is demonstrated, which is mainly used to adjust the spindle speed of the tool. Figure 4 The SetSpeed ​​module in the figure indicates that the target speed is set to 2000r / min. The target value is subtracted from the actual feed speed signal (feed_rate_signal) to obtain an error signal, which is adjusted by a PID controller. The PID controller parameters include proportional gain (Kp_Gain), integral gain (Ki_Gain) and differential gain (Kd_Gain). The proportional gain (Kp_Gain) is used to adjust the proportional part of the error signal, the integral gain (Ki_Gain) adjusts the integral part of the error signal through an integrator (1 / s), and the differential gain (Kd_Gain) adjusts the differential part of the error signal through a differentiator (Δu / Δt). Each gain module performs corresponding operations on the error signal and adds the results to generate a control signal.

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

[0149] The FOC (Field Oriented Control) module represents a first-order transfer function (1 / (0.1s+1)), which is used to simulate the dynamic response of the actual motor. The 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, which represents the actual spindle speed signal. It is used for feedback control system and compared with the set value to form a closed-loop control system to ensure that the spindle speed is stable near the expected value. Through error signal calculation, PID control, feedforward control, motor dynamic response and feedback loop, the Simulink model achieves accurate and efficient speed control to ensure that the spindle speed is stable at the set value of 2000r / min. The PID controller adjusts the error signal, the feedforward control compensates the dynamic error, and the FOC module simulates the dynamic response of the motor, finally achieving stable control of the system.

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

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

[0153] See also Figures 5 to 7 ,in, Figure 5 The curve showing the initial wall thickness of a thin-walled workpiece changing with time using feedforward control combined with PID control. Figure 6 The curve showing the spindle speed of a thin-walled workpiece changing with time using feedforward control combined with PID control. Figure 7 The curve showing the final wall thickness of a thin-walled workpiece changing over time with feedforward control combined with PID control. Figures 5 to 7 It can be seen that after adding feedforward control, the spindle speed responds faster, the cutting process is more stable, and the final wall thickness changes smoothly.

[0154] See also Figures 8 to 10 , Figure 8 This is the curve of the initial wall thickness changing with time under simple PID control. Fig. 9 This is the curve of the spindle speed changing with time under simple PID control. Fig.10 This is the curve of the final wall thickness changing with time under simple PID control. The spindle speed fluctuates greatly with time, the cutting process is not stable enough, and the final wall thickness change is not as stable 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 has better performance in improving cutting efficiency and reducing energy waste. The specific simulation results show that the total volume of removed material has increased significantly and the total energy waste has decreased, thus verifying the effectiveness of the optimized control parameters.

[0156] It can be seen from the above embodiments that, compared with the prior art, the present application aims at the conventional milling method of thin-walled workpieces in the prior art, which usually requires multiple trial cutting and adjustments on the actual machine tool, not only taking up a large amount of machine tool resources, but also due to the complex shape and material properties of the thin-walled workpiece, the processing success rate is often not high, and repeated adjustments and tests are required, which consumes a lot of time and human resources. The present application includes but is not limited to the following beneficial effects:

[0157] First, this application constructs a thin-walled workpiece milling processing framework based on digital twins, which uses 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 improves overall machining efficiency and product qualification rate by adjusting the dynamic control method of the 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 whale optimization algorithm, which has powerful global search capabilities and fast convergence characteristics, and can automatically optimize PID controller parameters and feedforward control to improve the control accuracy and response speed of the system.

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

[0161] See also Fig.11 A thin-walled workpiece milling control device provided to meet one of the purposes of the present application includes a data acquisition module 1100, an optimal 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 respond to a thin-walled workpiece milling vibration suppression instruction to determine a spectrum function, a system transfer function, a system damping ratio and a system damping frequency corresponding to the cutting force, wherein the system transfer function is constructed by the system natural frequency and the system damping ratio; the optimal speed determination module 1200 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, the system transfer function, the system damping ratio and the system damping frequency corresponding to the cutting force; the parameter optimization model construction module 1300 is configured to respond to a system control parameter optimization instruction to determine the total cutting material volume and the total energy consumption of the thin-walled workpiece. Consumption, based on the total cutting material volume and the total energy consumption, construct a system control parameter optimization model to minimize the total energy consumption and maximize the total cutting material volume; 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 system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control; a milling processing 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 to complete the processing control of thin-walled workpiece milling.

[0162] Based on any embodiment of this application, please refer to Fig.12Another embodiment of the present application further provides an electronic device, which can be implemented by a computer device, such as Fig.12 As shown, a schematic diagram of 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. Among them, the computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions. The database may store a control information sequence. 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 is used to provide computing and control capabilities to support 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 the present application. The network interface of the computer device is used to connect and communicate with a terminal. Those skilled in the art can understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0163] In this embodiment, the processor is used to execute Fig.11 The memory stores the program code and various data required to execute the above modules or submodules. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all modules / submodules in the thin-wall workpiece milling control device of this application, and the server can call the program code and data of the server to execute the functions of all submodules.

[0164] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the thin-wall workpiece milling control method described in any embodiment of the present application.

[0165] The present application also provides a computer program product, including a computer program / instruction, which, when executed by one or more processors, implements the steps of the thin-wall workpiece milling control method described in any embodiment of the present application.

[0166] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments of the present application can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a computer-readable storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0167] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present 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 is based on the whale optimization algorithm with powerful global search capabilities and fast convergence characteristics, and can automatically optimize PID controller parameters and feedforward control to improve the control accuracy and response speed of the system; This application is based on a method for solving the system control parameter optimization model based on a preset whale optimization algorithm. By determining the optimal combination of PID controller parameters and feedforward control, it can effectively improve cutting efficiency and reduce energy waste.

Claims

1. A thin-wall workpiece milling control method, characterized in that: include: In response to a thin-wall workpiece milling vibration suppression instruction, a frequency spectrum function, a system transfer function, a system damping ratio and a system damping frequency corresponding to the cutting force are determined, wherein the system transfer function is constructed by the system natural frequency and the system damping ratio; Based on the spindle speed control model for minimizing 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; In response to a system control parameter optimization instruction, determining a total cutting material volume and a total energy consumption of the thin-walled workpiece, and constructing a system control parameter optimization model for minimizing the total energy consumption and maximizing the total cutting material volume based on the total cutting material volume and the total energy consumption; Solving 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 by proportional gain, integral gain, differential gain and feedforward control; Based on the optimal spindle speed corresponding to the thin-walled workpiece spindle and the optimal system control parameters, the thin-walled workpiece to be milled is controlled to be processed to complete the processing control of the thin-walled workpiece milling.

2. The thin-wall workpiece milling control method according to claim 1, characterized in that: The steps to build the spindle speed control model include: Performing fast Fourier transform on the cutting force to determine a frequency spectrum function corresponding to the cutting force; 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 a simplified two-degree-of-freedom vibration model, and determine the natural frequency of the system 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 system damping ratio, and calculate and determine the system damping frequency according to the system natural frequency and the system damping ratio; Based on the natural frequency of the system and the damping ratio of the system, a transfer function of the system is constructed; Based on the frequency 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 thin-wall workpiece milling control method according to claim 2, characterized in that: The expression of the simplified two-degree-of-freedom vibration model is: Among them, among them, represents the vibration displacement of the thin-walled workpiece at time t, represents the vibration velocity of the thin-walled workpiece, x(t) represents the vibration acceleration of the thin-walled workpiece, m represents the equivalent mass of the thin-walled workpiece and the tool, c is the damping coefficient, k is the dynamic stiffness between the thin-walled workpiece and the tool, and F(t) represents the cutting force.

4. The thin-wall workpiece milling control method according to claim 2, characterized in that: The expression of the spectrum function corresponding to the cutting force is: F(w)=FFT(F(t)), Among them, F(w) represents the frequency spectrum function corresponding to the cutting force; The expression of the natural frequency of the system is: Among them, w n represents the natural frequency of the system; The expression of the damping frequency of the system is: Among them, w d represents the system damping frequency, ζ represents the system damping ratio; The expression of the transfer function of the system is: Wherein, w represents the spindle speed; The expression of the spindle speed control model to minimize system vibration is: min w |H(w)F(w)|。 5. The thin-wall workpiece milling control method according to claim 1, characterized in that: The steps of determining the total cutting material volume and the 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 include: The objective function of the total cutting material volume of the thin-walled workpiece is: Where A(t) is the cutting area varying with time, M total Indicates the total volume of cut material for thin-walled workpieces; The objective function of the total energy consumption of the thin-walled workpiece is: Among them, E waste represents the total energy consumption of the thin-walled workpiece, S(t) is the time-varying spindle speed, S op is the optimal speed. When the spindle speed is greater than the optimal speed, it will lead to a waste of power resources. The expression of the system control parameter optimization model is: f(x)=M total +E waste 。 6. The thin-wall workpiece milling control method according to claim 1, characterized in that: 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: Calling a preset whale optimization algorithm, solving the system control parameter optimization model based on the whale optimization algorithm, and determining initial parameters of the whale optimization algorithm, wherein the initial parameters include a population size and a number of iterations; Randomly initializing a whale population according to the population size to generate initial whale individuals, wherein the whale individuals represent a system control parameter combination 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 speed of each individual whale are updated according to the fitness objective function value; In the iterative process of the algorithm, the whale individual with the best fitness objective function value is selected as the optimal system control parameter combination; When the preset number of iterations is reached or the convergence condition is met, the optimization process is terminated to obtain the optimal system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control.

7. The thin-wall workpiece milling control method according to claim 5, characterized in that: The optimal rotation speed is 2000r / min.

8. A thin-wall workpiece milling control device, characterized in that: include: A data acquisition module is configured to respond to a vibration suppression instruction for milling a thin-walled workpiece and determine a frequency spectrum function, a system transfer function, a system damping ratio, and a system damping frequency corresponding to the cutting force, wherein the system transfer function is constructed by the system natural frequency and the system damping ratio; an optimal speed determination module, configured to determine an optimal spindle speed corresponding to a thin-walled workpiece spindle according to a spectrum function corresponding to the cutting force, a system transfer function, a system damping ratio, and a system damping frequency based on a spindle speed control model for minimizing system vibration; a parameter optimization model building module, configured to respond to a system control parameter optimization instruction, determine a total cutting material volume and a total energy consumption of the thin-walled workpiece, and build 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; An 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 a system control parameter combination constructed by proportional gain, integral gain, differential gain and feedforward control; The milling control module is configured to control the processing of the thin-walled workpiece to be milled based on the optimal spindle speed and optimal system control parameters corresponding to the thin-walled workpiece spindle, so as to complete the processing control of the thin-walled workpiece milling.

9. An electronic device, comprising a central processing unit and a memory, characterized in that: The central processing unit is used to call and run the computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It stores a computer program implemented according to the method described in any one of claims 1 to 7 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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