Milling process quality online prediction method and system based on MEC edge calculation

Abstract

The invention discloses a milling process quality online prediction method and system based on MEC edge calculation. The method comprises the following steps: constructing a domain adversarial neural network (DANN) based on MEC edge calculation to carry out process quality online prediction model theoretical framework; milling simulation data is used as source domain data, experimental data of a small sample is used as target domain data, and the maximum deformation of a workpiece is predicted by inputting milling three-element data; secondly, the DANN structure comprises a feature extractor, a regression predictor and a domain classifier, and the feature extractor is used for aligning common features of source domain data and target domain data; the domain classifier and the feature extractor form confrontation; the regression predictor establishes a mapping relation between the input data and the maximum deformation of the workpiece; accurate online prediction of the milling quality of the thin-walled workpiece is achieved, high-quality and high-efficiency machining of the workpiece is ensured, and technical support is provided for machining quality perception of a digital twin process model in a process planning stage.

Description

Background Art

[0002] Aerospace parts are widely used in aerospace equipment such as rockets and aircraft. They have the characteristics of thin-wall overhang and extremely large size ratio. Their processing quality and service performance directly determine whether the aviation equipment can operate safely and reliably. Milling is one of the main processes for processing thin-wall features and is the key to ensuring the processing quality of parts. At present, in order to adapt to the highly variable use environment of aerospace equipment, the shape and structure of thin-wall features are becoming increasingly complex, and the requirements for part milling quality are also constantly increasing. Among them, milling deformation caused by high material removal rate and continuous reduction of workpiece stiffness is one of the main factors affecting milling quality. In order to ensure the milling quality of thin-wall features, it is crucial for aerospace manufacturing companies to provide effective milling deformation perception, optimization and control methods.

[0003] However, it is difficult to collect quality data for complex aviation parts, and it is very challenging to accurately predict the milling deformation of thin-wall features under a small sample data source, resulting in low efficiency in thin-wall feature processing and uncontrollable processing cycles. The digital twin-based processing process has made some progress in building an intelligent milling process, such as time-varying error control and cutting parameter optimization. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an online prediction method and system for milling process quality based on MEC edge computing in response to the above-mentioned deficiencies in the prior art, so as to solve the technical problems of low processing efficiency of thin-wall features and uncontrollable processing cycle.

[0005] The present invention adopts the following technical solutions: The online prediction method of milling process quality based on MEC edge computing includes the following steps: Construct a theoretical framework for online prediction of milling process quality based on MEC edge computing; The processing data acquisition module in the theoretical framework of online prediction of milling process quality adopts the finite element method to establish a virtual simulation model of thin-wall features under the action of milling force, using milling simulation data as source domain data and small sample experimental data as target domain data; Based on the domain adversarial neural network model, the milling deformation prediction module in the theoretical framework of online prediction of milling process quality is deployed at the MEC edge. By inputting the spindle speed, feed speed and cutting depth data of milling, the maximum deformation of the workpiece is predicted, thereby realizing the online prediction of the milling quality of thin-walled parts.

[0006] Preferably, the milling deformation prediction module includes: Feature extractor, used to extract common features of the source domain and the target domain, and align data samples of the source domain and the target domain in the feature space; A regression predictor is used to make predictions based on the mapping relationship between the domain-invariant features extracted from the source and target domain data and the maximum deformation of the workpiece; The domain classifier discriminates the features of the source domain and target domain data through the binary cross entropy loss function, realizes the domain label classification of the features generated by the feature extractor, and forms an adversarial relationship with the feature extractor.

[0007] Preferably, the domain invariant feature is implemented by a gradient reversal layer in a domain adversarial neural network. The forward and backward propagation processes of the gradient reversal layer are as follows:

[0008]

[0009] in, The gradient reversal layer can be regarded as a pseudo function. is the input feature, I is the identity matrix, α is a positive hyperparameter.

[0010] Preferably, the minimization loss function of the regression predictor is as follows:

[0011] in, m is the batch size of training samples, and are the actual label value and the predicted label value, respectively. is the network connection weight of the feature extractor, are the network connection weights of the regression predictor.

[0012] Preferably, the optimization function of the domain classifier is as follows:

[0013] in, for, is the learning rate, is the binary cross entropy loss function.

[0014] Preferably, the binary cross entropy loss function is for:

[0015] in, is the actual domain label, is the predicted domain label, is the batch size of training samples.

[0016] Preferably, the optimization function By searching for the minimum point , , Optimize so that , , , for , , saddle point.

[0017] Preferably, The saddle point for: .

[0019] Preferably, the learning weights in the domain adversarial neural network model are updated by gradient descent, expressed as:

[0020]

[0021]

[0022] in, λ represents the learning rate, , , is the minimization point, is the regression loss, is the domain classification loss.

[0023] In a second aspect, an embodiment of the present invention provides an online prediction system for milling process quality based on MEC edge computing, including: Building modules, constructing a theoretical framework for online prediction of milling process quality based on MEC edge computing; The simulation module, the processing data acquisition module in the theoretical framework of online prediction of milling process quality, uses the finite element method to establish a virtual simulation model of thin-wall features under the action of milling force, using the milling simulation data as the source domain data and the experimental data of a small sample as the target domain data; The prediction module, based on the domain adversarial neural network model, deploys the milling deformation prediction module in the online prediction theoretical framework of milling process quality at the MEC edge. By inputting the spindle speed, feed speed and cutting depth data of the milling process, the maximum deformation of the workpiece is predicted, thereby realizing the online prediction of the milling quality of thin-walled parts.

[0024] In the third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for online prediction of milling process quality based on MEC edge computing when executing the computer program.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for online prediction of milling process quality based on MEC edge computing.

[0026] Compared with the prior art, the present invention has at least the following beneficial effects: Based on the online prediction method of milling process quality of MEC edge computing, a theoretical framework of online prediction of milling process quality based on MEC edge computing was constructed. Then, the milling process quality prediction model using domain adversarial neural network DANN was deployed on the edge of MEC. With the help of the powerful edge computing power of MEC, the simulation data of milling simulation was used as the source domain data, and the small sample experimental data under the processing test conditions was used as the target domain data. Combined with the domain adversarial neural network DANN, by inputting the three elements of milling cutting data, the maximum deformation of the milling workpiece is predicted, which provides important technical support for the twin process model of the milling process in the process planning stage. The processing quality perception can achieve high-quality and efficient processing of the workpiece.

[0027] Furthermore, the domain adversarial neural network is combined to predict the milling quality. The common features of the source data and the target domain data are extracted by the feature extractor, which can realize the alignment of samples in the feature space.

[0028] Furthermore, by using a domain classifier to determine whether the data comes from the source domain or the target domain, the samples of the two domains can be separated in the feature space, thereby confronting the feature extractor.

[0029] Furthermore, through the regression predictor, the mapping relationship between the input data (the three elements of milling processing) and the output data (the maximum deformation of the workpiece during milling processing) is found to predict the maximum deformation of the workpiece and realize the online prediction of the milling process quality of thin-walled parts.

[0030] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0031] To sum up, the present invention can accurately predict the milling deformation of the workpiece based on MEC edge computing, thereby ensuring high-quality and high-efficiency processing of the workpiece.

[0032] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings to be used in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 It is a theoretical framework diagram of the present invention; Figure 2 This is the flow chart of the milling deformation prediction method based on DANN; Figure 3 Figure 1. Setup diagram for milling experiment. Figure 4 Online prediction results for milling process quality; Figure 5 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 6 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0037] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0038] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0039] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0040] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0041] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0042] The present invention provides an online prediction method for milling process quality based on MEC edge computing, which is aimed at the process processing of aviation parts. By integrating the simulation data and actual processing data of the virtual space and physical space of the digital twin process model, combined with the multi-access edge computing (MEC) technology, and adopting the domain adversarial neural network (DANN), a small amount of actual deformation data is collected by deformation test as the target domain data, and the simulation data generated by milling simulation is used as the source domain data; in the milling process of thin-walled parts, the online accurate prediction of the milling deformation of thin-wall features is realized; the demand for efficient prediction of processing quality under small sample conditions is met, and technical guarantee is provided for the realization of integrated decision-making and quality control of the digital twin process model in the process planning stage.

[0043] See also Figure 1 The present invention provides an online prediction method for milling process quality based on MEC edge computing, comprising the following steps: S1. Construct a theoretical framework for online prediction of milling process quality based on MEC edge computing. The theoretical framework for online prediction of milling process quality includes a processing data acquisition module and a milling deformation prediction module. The milling deformation prediction module includes a feature extractor ( G f ), regression predictor ( G r ) and domain classifier ( G d ), the feature extractor ( G f ) is used to extract the common features of the source domain and the target domain and align the data samples of the source domain and the target domain in the feature space.

[0044] Regression predictor ( G r ) is predicted based on the mapping relationship between the domain invariant features extracted from the source domain and target domain data and the maximum deformation of the workpiece. The domain invariant features are implemented through the gradient reversal layer (GRL) in the domain adversarial neural network.

[0045] Domain-invariant features are extracted through domain adversarial operations. GRL keeps the input unchanged during forward propagation and reverses the gradient by multiplying the gradient by a negative scalar during backpropagation. GRL can be modeled as a "pseudo-function" R λ , its forward and backward propagation process is as follows: (1) (2) in, I is the identity matrix, α is a positive hyperparameter that achieves a trade-off between regression loss and domain classification loss.

[0046] Regression loss: The milling deformation prediction module seeks a direct mapping between the features derived from the source domain data and the deformation values. The loss function is minimized, which is expressed as the mean square error (MSE): (3) in, m is the batch size of training samples, and are the actual label value and the predicted label value respectively.

[0047] Domain Classifier ( G d ) The binary cross entropy loss function is used to discriminate the features of the source domain and target domain data, and the feature extractor ( G f ) to generate domain labels for features and combine them with the feature extractor ( G f ) to form a confrontation.

[0048] Domain classification loss: If the domain classifier maximizes the domain classification error and cannot distinguish features between the source and target domains, then the features are considered domain invariant. While the feature miner is trained to fool the domain classifier, the domain classifier is trained to discriminate the domain labels (source: 0, target: 1) of the features created by the feature identifier. The classification loss of the domain labels is defined as the binary cross entropy loss, and the domain classifier aims to minimize the domain classification loss. The function of the binary cross entropy loss is expressed as: (4) in, is the actual domain label, is the predicted domain label, discriminant Whether it is from the source domain or the target domain.

[0049] During the confrontation, G d and G f The sign of the loss function is opposite 。G d The goal of is to minimize its loss function, which is usually the domain classification loss of the source and target domains. G f The goal of is to maximize its loss function, which is usually the negative of the domain classification loss. By modifying the coefficients of the loss function, G d and Gf The final optimization function is summarized as follows: (5) Loss Function By searching for the minimum point , , Optimize so that: (6) (7) During the training process, the learning weights in the DANN model are updated by gradient descent, expressed as: (8) (9) (10) in, λ Represents the learning rate.

[0050] Feature Extractor ( G f ) By adjusting the weights and biases, the loss function between the calculated expected deformation value and the actual deformation value is minimized.

[0051] S2, the data acquisition module uses the digital twin process model and the finite element method to establish a virtual simulation model of thin-wall features under milling force, using the milling simulation data as the source domain data and the small sample experimental data under the processing test conditions as the target domain data; S3. Based on the domain adversarial neural network (DANN) model, the online prediction module for milling deformation is deployed at the edge of MEC. By inputting the data of the three elements of milling processing (spindle speed, feed speed, and cutting depth), the maximum deformation of the workpiece can be accurately predicted.

[0052] Regression predictor ( G r ) By modeling the relationship between input data (three elements of milling) and output data (maximum deformation of the workpiece), online prediction of the milling process quality of thin-walled parts can be achieved.

[0053] MEC edge computing provides distributed computing capabilities and supports online prediction of deformation during milling processing at the edge to ensure that the workpiece meets the design requirements.

[0054] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0055] In another embodiment of the present invention, an online prediction system for milling process quality based on MEC edge computing is provided. The system can be used to implement the above-mentioned online prediction method for milling process quality based on MEC edge computing. Specifically, the online prediction system for milling process quality based on MEC edge computing includes a construction module, a simulation module and a prediction module.

[0056] Among them, the construction module builds a theoretical framework for online prediction of milling process quality based on MEC edge computing; The simulation module, the processing data acquisition module in the theoretical framework of online prediction of milling process quality, uses the finite element method to establish a virtual simulation model of thin-wall features under the action of milling force, using the milling simulation data as the source domain data and the experimental data of a small sample as the target domain data; The prediction module, based on the domain adversarial neural network model, deploys the milling deformation prediction module in the online prediction theoretical framework of milling process quality at the MEC edge. By inputting the spindle speed, feed speed and cutting depth data of the milling process, the maximum deformation of the workpiece is predicted, thereby realizing the online prediction of the milling quality of thin-walled parts.

[0057] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the online prediction method of milling process quality based on MEC edge computing, including: A theoretical framework for online prediction of milling process quality based on MEC edge computing is constructed; the processing data acquisition module in the theoretical framework for online prediction of milling process quality adopts the finite element method to establish a virtual simulation model of thin-wall features under the action of milling force, and uses the milling simulation data as the source domain data and the experimental data of a small sample as the target domain data; based on the domain adversarial neural network model, the milling deformation prediction module in the theoretical framework for online prediction of milling process quality is deployed at the MEC edge. By inputting the spindle speed, feed speed and cutting depth data of the milling process, the maximum deformation of the workpiece is predicted, thereby realizing the online prediction of the milling quality of thin-walled parts.

[0058] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0059] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0060] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0061] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for online prediction of milling process quality based on MEC edge computing in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: A theoretical framework for online prediction of milling process quality based on MEC edge computing is constructed; the processing data acquisition module in the theoretical framework for online prediction of milling process quality adopts the finite element method to establish a virtual simulation model of thin-wall features under the action of milling force, and uses the milling simulation data as the source domain data and the experimental data of a small sample as the target domain data; based on the domain adversarial neural network model, the milling deformation prediction module in the theoretical framework for online prediction of milling process quality is deployed at the MEC edge. By inputting the spindle speed, feed speed and cutting depth data of the milling process, the maximum deformation of the workpiece is predicted, thereby realizing the online prediction of the milling quality of thin-walled parts.

[0062] See also Figure 5 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the online prediction method of the milling process quality based on MEC edge computing in the embodiment is implemented. To avoid repetition, it is not described here one by one. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the online prediction system of the milling process quality based on MEC edge computing in the embodiment are implemented. To avoid repetition, it is not described here one by one.

[0063] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0064] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0065] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0066] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0067] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0068] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0069] See also Figure 6 The terminal device is an electronic device 600, and the electronic device is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0070] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 as shown in .

[0071] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0072] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0073] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0074] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0075] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0076] See also Figure 3 , through the experimental setting, in the processing data acquisition module, the simulation data of milling simulation is used as the source domain data, and the small sample experimental data under the processing test conditions is used as the target domain data. The proposed method has been verified on Python 3.8. A 7050 aluminum alloy thin-walled part is selected for plane milling. The one-step milling cutter path is a rectangle of 80×20mm, and the tool is a three-tooth carbide with a diameter of 10mm. The simulation is carried out using ABAQUS 2022. The processing process is carried out on Dalian Machine Tool VDL650A and Global Classic SR 05.07.05. A three-coordinate measuring machine (measuring range 500*700*500mm, MPEP≤1.9μm) is used to measure the maximum deformation value of the workpiece after each processing. In order to predict the maximum deformation of milling, the present invention uses cutting parameters as input parameters. Cutting parameters (spindle speed, feed per tooth, axial depth), feed speed and maximum deformation of the workpiece are all included in the milling data set. The dynamic simulation of the milling process generated sufficient source data, and data extraction was performed after the simulation (a total of 120 groups). In order to provide appropriate target data according to the actual machining situation, 60 different machining parameters were selected to measure the workpiece deformation during the milling process.

[0077] See also Figure 4 , which is the experimental result of online prediction of milling process quality; in order to facilitate training, in the model training and testing stages, 120 sets of calibrated simulation data are used as source samples, and 60 sets of experimental data are selected as target samples. At the same time, 4-fold cross validation is used to evenly divide the 60 sets of target domain data into 4 parts, each containing 15 sets of target domain data. Among them, 1 part is used for model testing, and the remaining 3 sets of target data are used for training. The true value and predicted value of the maximum deformation of the workpiece are shown in Figure 4 As shown in the figure, the average relative errors of each fold are 3.21%, 4.69%, 4.45% and 4.17%, respectively, all less than 5%, and the prediction accuracy is above 95%, which verifies the effectiveness of the proposed method.

[0078] In summary, the present invention provides an online prediction method and system for milling process quality based on MEC edge computing, and constructs a domain adversarial neural network (DANN) theoretical framework based on MEC edge computing for online prediction of process quality. Specifically, milling simulation data is used as source domain data, and combined with small sample experimental data as target domain data, the maximum deformation of the workpiece is predicted by inputting the three-element data of milling processing; the DANN structure includes a feature extractor, a regression predictor, and a domain classifier; the feature extractor is used to align the common features of the source domain and target domain data, the domain classifier forms an adversarial relationship with the feature extractor, and the regression predictor establishes a mapping relationship between the input data and the maximum deformation of the workpiece. Finally, the system can accurately predict the milling quality of thin-walled parts online, ensure high-quality and efficient processing of workpieces, and provide effective technical support for the processing quality perception of the digital twin process model in the process planning stage.

[0079] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0080] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0082] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0085] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0089] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. The online prediction method of milling process quality based on MEC edge computing is characterized by: The following steps are involved: Construct a theoretical framework for online prediction of milling process quality based on MEC edge computing; The processing data acquisition module in the theoretical framework of online prediction of milling process quality adopts the finite element method to establish a virtual simulation model of thin-wall features under the action of milling force, using milling simulation data as source domain data and small sample experimental data as target domain data; Based on the domain adversarial neural network model, the milling deformation prediction module in the theoretical framework of online prediction of milling process quality is deployed at the MEC edge. By inputting the spindle speed, feed speed and cutting depth data of milling, the maximum deformation of the workpiece is predicted, thereby realizing the online prediction of the milling quality of thin-walled parts.

2. The method for online prediction of milling process quality based on MEC edge computing according to claim 1 is characterized in that: The milling deformation prediction module includes: Feature extractor, used to extract common features of the source domain and the target domain, and align data samples of the source domain and the target domain in the feature space; A regression predictor is used to make predictions based on the mapping relationship between the domain-invariant features extracted from the source and target domain data and the maximum deformation of the workpiece; The domain classifier discriminates the features of the source domain and target domain data through the binary cross entropy loss function, realizes the domain label classification of the features generated by the feature extractor, and forms an adversarial relationship with the feature extractor.

3. The method for online prediction of milling process quality based on MEC edge computing according to claim 2 is characterized in that: The domain-invariant feature is implemented through the gradient reversal layer in the domain adversarial neural network. The forward and backward propagation processes of the gradient reversal layer are as follows: in, The gradient reversal layer can be regarded as a pseudo function. is the input feature, I is the identity matrix, α is a positive hyperparameter.

4. The method for online prediction of milling process quality based on MEC edge computing according to claim 2 is characterized in that: The minimization loss function for the regression predictor is as follows: in, m is the batch size of training samples, and are the actual label value and the predicted label value, respectively. is the network connection weight of the feature extractor, are the network connection weights of the regression predictor.

5. The method for online prediction of milling process quality based on MEC edge computing according to claim 2 is characterized in that: Optimization function for domain classifier as follows: in, for, is the learning rate, is the binary cross entropy loss function.

6. The method for online prediction of milling process quality based on MEC edge computing according to claim 5 is characterized in that: Binary Cross Entropy Loss Function for: in, is the actual domain label, is the predicted domain label, is the batch size of training samples.

7. The method for online prediction of milling process quality based on MEC edge computing according to claim 5 is characterized in that: Optimizing functions By searching for the minimum point , , Optimize so that , , , for , , saddle point.

8. The method for online prediction of milling process quality based on MEC edge computing according to claim 7 is characterized in that: The saddle point for: 。 9. The method for online prediction of milling process quality based on MEC edge computing according to claim 1 is characterized in that: The learning weights in the domain adversarial neural network model are updated by gradient descent, expressed as: in, λ represents the learning rate, , , is the minimization point, is the regression loss, is the domain classification loss.

10. An online prediction system for milling process quality based on MEC edge computing, characterized in that: include: Building modules, constructing a theoretical framework for online prediction of milling process quality based on MEC edge computing; The simulation module, the processing data acquisition module in the theoretical framework of online prediction of milling process quality, uses the finite element method to establish a virtual simulation model of thin-wall features under the action of milling force, using the milling simulation data as the source domain data and the experimental data of a small sample as the target domain data; The prediction module, based on the domain adversarial neural network model, deploys the milling deformation prediction module in the online prediction theoretical framework of milling process quality at the MEC edge. By inputting the spindle speed, feed speed and cutting depth data of the milling process, the maximum deformation of the workpiece is predicted, thereby realizing the online prediction of the milling quality of thin-walled parts.

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