An integrated composite machining process planning method for a special-shaped pipe

Abstract

The present application relates to the technical field of pipe processing, and particularly relates to an integrated composite processing process planning method for special-shaped pipes, which comprises the following steps: obtaining technical information of a target product, importing a preset composite processing process chain of the target product based on the technical information, optimizing the composite processing process chain to reduce multi-process conflicts in the composite processing process chain and reduce process cumulative errors, comprising the following steps: quantifying physical coupling chains among multiple processes in the composite processing process chain, decoupling the physical coupling chains through a decoupling strategy to reduce the multi-process conflicts, and after the decoupling of the physical coupling chains is completed, balancing error control and time optimization of the composite processing process chain through an error accumulation suppression and dynamic precision reset strategy.

Description

Technical Field

[0001] This invention relates to the field of pipe processing technology, and in particular to an integrated composite processing technology planning method for irregularly shaped pipes. Background Technology

[0002] Shaped tubes refer to tubes whose cross-sectional shape is not a standard circle. They are distinguished from the most common round tubes. In shaped tube processing technology, integrated composite processing is a highly integrated and automated advanced manufacturing model. It integrates multiple processing techniques (such as forming, welding, cutting, punching, bending, etc.) onto a single piece of equipment or an intelligent production line to achieve continuous, efficient, and high-precision production of shaped tubes. This technology is an important direction for the manufacturing industry's transformation towards intelligence and flexibility, and is especially suitable for the mass production or customized production of complex shaped tubes.

[0003] In the integrated composite processing of irregularly shaped tubes, process planning is the core link to ensure efficient and precise production. It requires comprehensive consideration of material properties, target geometry, precision requirements, production volume, and cost constraints, integrating and optimizing multiple processes (forming, welding, cutting, punching, etc.) through a systematic approach. Existing technologies typically address process conflicts and error accumulation by analyzing resource contention, optimizing scheduling logic, and setting reset stations. However, due to the physical coupling effect and time dependence between processes, optimizing scheduling logic solely from the perspective of resource contention cannot fundamentally solve the problems of multi-process conflicts and waiting time. Summary of the Invention

[0004] This invention solves the problem of hidden interference between processes by modeling the physical coupling effect between processes and decoupling them through physical state coupling, thereby reducing processing time while ensuring quality in the composite processing chain.

[0005] The technical solution proposed in this invention is: an integrated composite processing technology planning method for irregularly shaped tubes, the method comprising:

[0006] Obtain the technical information of the target product, and import the pre-set composite processing chain of the target product based on the technical information;

[0007] Optimize the complex machining process chain to reduce multi-process conflicts and decrease cumulative process errors; including:

[0008] The physical coupling chain between multiple processes in a quantitative composite processing chain is decoupled through a decoupling strategy to reduce conflicts between multiple processes.

[0009] After the physical coupling chain is decoupled, error control and time optimization of the composite machining process chain are balanced through error accumulation suppression and dynamic precision reset strategies.

[0010] Preferably, the step of importing the pre-defined composite processing chain of the target product based on technical information includes:

[0011] Product cross-sectional features, curvature features, and wall thickness features are extracted from product technical information to form a product complexity dataset.

[0012] Product dimensional tolerances and geometric tolerances are obtained from product technical information to form a product accuracy dataset;

[0013] The product's yield strength and ductility are obtained from the product's technical information to form a material property dataset;

[0014] Based on the product's technical information, determine whether it is an irregularly shaped tube. If so, import the preset composite processing chain; otherwise, trigger a data anomaly alarm and reacquire the technical information of the target product.

[0015] Preferably, the physical coupling chain between multiple processes in the quantitative composite processing chain includes:

[0016] Assume a complex processing chain includes processes ;in Indicates the number of processes;

[0017] Each process outputs a state vector: ; ;in, Represents the residual stress tensor; Indicates cumulative plastic strain; Represents the temperature field; Represents the geometric deviation vector;

[0018] State transfer equation ; Indicates the first The physical evolution function of each process step; Indicates the first Control inputs for each process; Indicates random noise;

[0019] The physical coupling chain is then quantized as follows: .

[0020] Preferably, the decoupling of the physical coupling chain through the decoupling strategy includes:

[0021] Sensing process status, including:

[0022] Establish observation equations ;in, Represents the observation matrix; Indicates measurement noise; Represents sensor data;

[0023] Predict the process status and update the process status using Kalman filtering:

[0024] ; where the Kalman gain matrix ; Indicates the sensor noise covariance; This represents the predicted state value at the previous time step. Represents the state prediction covariance matrix;

[0025] Decouple the physical coupling chain between processes, i.e. perform feedforward compensation to cancel the interference from the previous process, so that the output state vector of each process is close to the actual state vector.

[0026] Construct compensator To adjust the control input;

[0027] Decoupling function ;in, Represents the decoupling gain matrix. Indicates the stress attenuation memory factor; This indicates the penalty coefficient for exceeding temperature limits; Represents the geometric deviation projection matrix; Indicate process The residual stress value; Indicates a safe temperature field;

[0028] Insert a reset factor to reset the state:

[0029] ;in, Indicates stress relief rate; Represents the geometric correction matrix; Indicates the reset operator;

[0030] Calculate the rate of decrease in coupling. ;in This represents the final output state vector of the decoupled composite processing chain. Represents the ideal output state vector; This represents the final output state vector of the composite processing chain when decoupling is not performed;

[0031] if The determination that decoupling reduces the interference of the previous process on the current process state is then made.

[0032] Preferably, after the physical coupling chain is decoupled, the error control and time optimization of the composite machining process chain are balanced through error accumulation suppression and dynamic accuracy reset strategies, including:

[0033] Construct error accumulation suppression and dynamic accuracy reset strategies;

[0034] Perform reset requirement prediction, and determine whether the reset conditions are met based on the error accumulation suppression and dynamic precision reset strategy. If so, perform reset; otherwise, do not reset.

[0035] When performing a reset, the reset strength is adaptively adjusted.

[0036] Time compensation is performed.

[0037] Preferably, the step of predicting the reset requirement, based on an error accumulation suppression and dynamic precision reset strategy, determines whether the reset conditions are met, including:

[0038] Construct the error accumulation dynamic equation:

[0039] Let the first The output error of each process is ,in , Indicates the first Error propagation matrix of each process step; Indicates the first Process control input matrix, Indicates random perturbation;

[0040] Defined in The decision variable for resetting the process step is: ;in, This represents the reset demand index; Indicates the dynamic decision threshold;

[0041] Predicting the error sensitivity of subsequent processes ;in, Indicates the current error. Indicates future risks; Represents the error weight matrix; error accumulation and propagation operator. , Indicates the criticality of process quality. ; , Indicates the balance coefficient; Indicates the first j Error propagation matrix of each process step;

[0042] Dynamic decision threshold ;in, Indicates the reset time; Indicate process Standard beat; , Indicates the adjustment parameter; Indicates the cumulative number of resets;

[0043] The error accumulation suppression and dynamic accuracy reset strategy includes:

[0044] Get Value, if Then in the process A reset station is added at the location for reset;

[0045] if If not, then no reset will be performed.

[0046] Preferably, when performing a reset, adaptively adjusting the reset strength includes:

[0047] when At that time, for the first The output error of each process is reset;

[0048] The error after reset, i.e., the reset strength. ;

[0049] Among them, the adaptive reset matrix ; This represents the Sigmoid function. , Indicates the error limit threshold. Indicates the width of the transition interval.

[0050] Preferably, the time compensation includes:

[0051] if Start accelerated measurement:

[0052] Calculate compensation time ;in, Indicates the reset duration. Indicate process The buffer time between the next process and the next process; Indicates the buffer extraction coefficient;

[0053] Record the compensation time and the process The buffer time between the current process and the next process is adjusted to This is to compensate for the time consumed by the reset station.

[0054] Preferred options also include:

[0055] Based on process complexity, differentiate between irregularly shaped and non-irregularly shaped tubes, and optimize process resource allocation and process costs, including:

[0056] Quantifying process complexity:

[0057] process complexity ;in, , , Represents the shape complexity coefficient, process complexity coefficient, and material complexity coefficient;

[0058] Shape complexity ; This represents the actual perimeter of the cross-section. Minimum circumference of the circumcircle Indicates the rate of change of cross-sectional area. Indicates the average cross-sectional area;

[0059] Irregularly shaped tubes and non-irregularly shaped tubes are distinguished based on their shape complexity, i.e., when If the shape is irregular, it is determined to be an irregularly shaped tube; otherwise, it is determined to be a non-irregularly shaped tube.

[0060] Process complexity ,in, ; Indicates the weight of device type;

[0061] Material complexity ;in, Indicates the elastic modulus. Indicates the yield strength of the material. Indicates the density of the material. Indicates the melting point of the material;

[0062] Based on process complexity, optimize resource allocation and process costs, namely:

[0063] Process ;in, This represents the sensitivity coefficient of resources to complexity. Indicates the total number of resources;

[0064] Process Process cost ;in, Indicates the cost coefficient;

[0065] when At that time, stop optimizing resource allocation and process costs.

[0066] An electronic device includes a processor, a communication module connected to the processor, and a memory, the electronic device being used to implement the integrated composite processing technology planning method for a non-standard tube.

[0067] The beneficial effects of this invention are:

[0068] This invention transforms hidden interferences into explicit, controllable parameters, i.e., control inputs for each process, by quantifying the physical coupling chains between processes; and by designing compensation quantities through decoupling strategies to counteract interference from previous processes, thus solving the problem of multi-process conflicts. Attached Figure Description

[0069] Figure 1 This is a flowchart of an integrated composite processing method for irregularly shaped tubes according to the present invention. Detailed Implementation

[0070] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0071] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0072] Example 1:

[0073] refer to Figure 1 The technical solution provided by this invention is: an integrated composite processing technology planning method for irregularly shaped tubes, the method comprising:

[0074] Step 1: Obtain the technical information of the target product and import the pre-set composite processing chain of the target product based on the technical information; specifically, this includes the following steps:

[0075] Product cross-sectional features, curvature features, and wall thickness features are extracted from product technical information to form a product complexity dataset; cross-sectional features include cross-sectional shape, cross-sectional area, and cross-sectional perimeter;

[0076] Product dimensional tolerances and geometric tolerances are obtained from product technical information to form a product accuracy dataset;

[0077] The product's yield strength and ductility are obtained from the product's technical information to form a material property dataset;

[0078] Based on the product's technical information, determine whether it is an irregularly shaped tube. If so, import the preset composite processing chain; otherwise, trigger a data anomaly alarm and reacquire the technical information of the target product.

[0079] Step 2: Optimize the complex machining process chain to reduce conflicts between multiple processes and decrease cumulative process errors. This includes the following steps:

[0080] Step 2.1: Quantify the physical coupling chain between multiple processes in the composite processing chain, and decouple the physical coupling chain through decoupling strategies to reduce conflicts between multiple processes;

[0081] The physical coupling chain between multiple processes in the quantitative composite processing chain includes the following steps:

[0082] Assume a complex processing chain includes processes ;in Indicates the number of processes;

[0083] Each process outputs a state vector: ; ;in, Represents the residual stress tensor; Indicates cumulative plastic strain; Represents the temperature field; Represents the geometric deviation vector;

[0084] State transfer equation ; Indicates the first The physical evolution function of each process step; Indicates the first Control inputs for each process; Indicates random noise;

[0085] The physical coupling chain is then quantized as follows: .

[0086] For example, suppose For the welding process, the state of the previous process. (Including initial residual stress) ); Control input ;in, ;

[0087] Heat conduction is triggered by an electric arc heat source; due to the restriction of thermal expansion, the pipe undergoes plastic strain. Tensile stress is formed after cooling. ;

[0088] Non-uniform shrinkage leads to geometric deformation ; express Deformation in the axial direction Deformation along the axis and angular deformation;

[0089] Physical coupling chain ; + .

[0090] The decoupling strategy for the physical coupling chain includes the following steps:

[0091] Sensing process status, including:

[0092] Establish observation equations ;in, The observation matrix is ​​represented (determined by the sensor layout). Indicates measurement noise; Represents sensor data;

[0093] Predict the process status and update the process status using Kalman filtering:

[0094] ; where the Kalman gain matrix ; Indicates the sensor noise covariance; This represents the predicted state value at the previous time step. Let represent the state prediction covariance matrix, and let represent the prediction error uncertainty based on the state estimation at the previous time step.

[0095] Decouple the physical coupling chain between processes, i.e. perform feedforward compensation to cancel the interference from the previous process, so that the output state vector of each process is close to the actual state vector.

[0096] Construct compensator To adjust the control input;

[0097] Decoupling function ;in, Represents the decoupling gain matrix. Indicates the stress attenuation memory factor; This indicates the penalty coefficient for exceeding temperature limits; Represents the geometric deviation projection matrix; Indicate process The residual stress value; Indicates a safe temperature field;

[0098] Insert a reset factor to reset the state, i.e., in critical processes. Forced state regression benchmark:

[0099] ;in, This indicates the stress relief rate (e.g., 0.7 means 70% of the stress has been relieved). Represents the geometric correction matrix (generated by least squares fitting); Indicates the reset operator;

[0100] Calculate the rate of decrease in coupling. ;in This represents the final output state vector of the decoupled composite processing chain. Represents the ideal output state vector; This represents the final output state vector of the composite processing chain when decoupling is not performed;

[0101] if Therefore, it is determined that decoupling reduces the interference of the previous process on the current process state; for example... .

[0102] To suppress error accumulation, existing technologies typically address this by adding precision reset stations during process planning. However, adding precision reset stations increases cycle time, while eliminating them reduces process quality due to error accumulation. Therefore, selective reset is implemented by optimizing the process across quality, efficiency, and cost. Specifically:

[0103] Step 2.2: After the physical coupling chain is decoupled, the error control and time optimization of the composite machining process chain are balanced through error accumulation suppression and dynamic accuracy reset strategies. This specifically includes the following steps:

[0104] Step 2.2.1: Construct error accumulation suppression and dynamic accuracy reset strategies;

[0105] Reset requirement prediction is performed. Based on the error accumulation suppression and dynamic precision reset strategy, it is determined whether the reset conditions are met. If so, a reset is performed; otherwise, no reset is performed. Specifically:

[0106] Construct the error accumulation dynamic equation:

[0107] Let the first The output error of each process is ,in , Indicates the first Error propagation matrix of each process step; Indicates the first Process control input matrix, Indicates random perturbation;

[0108] Defined in The decision variable for resetting the process step is: ;in, This represents the reset demand index; Indicates the dynamic decision threshold;

[0109] Predicting the error sensitivity of subsequent processes ;in, Indicates the current error. Indicates future risks; Represents the error weight matrix (determined by quality requirements); error accumulation and propagation operator. , Indicates the criticality of process quality. ; , Indicates the balance coefficient; Indicates the first jError propagation matrix of each process step;

[0110] Dynamic decision threshold ;in, Indicates the reset time; Indicate process Standard beat; , Indicates the adjustment parameter; Indicates the cumulative number of resets;

[0111] The error accumulation suppression and dynamic accuracy reset strategy includes:

[0112] Get Value, if Then in the process A reset station is added at the location for reset; if If not, then no reset will be performed.

[0113] Step 2.2.2: When performing a reset, adaptively adjust the reset strength; specifically including the following steps:

[0114] when At that time, for the first The output error of each process is reset;

[0115] The error after reset, i.e., the reset strength. ;

[0116] Among them, the adaptive reset matrix ; This represents the Sigmoid function. , Indicates the error limit threshold. Indicates the width of the transition interval.

[0117] Step 2.2.3: After completing the reset, perform time compensation, which includes the following steps:

[0118] if Start accelerated measurement:

[0119] Calculate compensation time ;in, Indicates the reset duration. Indicate process The buffer time between the next process and the next process; This represents the buffer extraction factor (values ​​range from 0.6 to 0.8).

[0120] Record the compensation time and the process The buffer time between the current process and the next process is adjusted to This is to compensate for the time consumed by the reset station.

[0121] Example 2:

[0122] In Example 1, to address process conflicts and error accumulation during process planning, the physical coupling effects (such as thermal deformation and material springback) and time-dependent factors (such as cooling aging) in the composite processing of irregularly shaped tubes were considered, thus overcoming the limitations of existing technologies that rely solely on resource scheduling to handle process conflicts. However, its optimization process is limited to the dimensions of quality (cumulative error) and efficiency (cycle time). This example, building upon Example 1, adds the dimensions of process complexity and cost, effectively distinguishing between irregularly shaped tube and non-irregularly shaped tube processes, while avoiding the problem of infinite optimization. The specific solution is as follows:

[0123] Based on the product complexity dataset and material property dataset, the process complexity is calculated and obtained, including:

[0124] Quantifying process complexity:

[0125] process complexity ;in, , , Represents the shape complexity coefficient, process complexity coefficient, and material complexity coefficient;

[0126] Shape complexity ; This represents the actual perimeter of the cross-section. Minimum circumference of the circumcircle Indicates the rate of change of cross-sectional area. Indicates the average cross-sectional area;

[0127] Irregularly shaped tubes and non-irregularly shaped tubes are distinguished based on their shape complexity, i.e., when If the shape is irregular, it is determined to be an irregularly shaped tube; otherwise, it is determined to be a non-irregularly shaped tube.

[0128] Process complexity ,in, ; Indicates the weight of device type;

[0129] Material complexity ;in, Indicates the elastic modulus. Indicates the yield strength of the material. Indicates the density of the material. Indicates the melting point of the material;

[0130] Based on process complexity, optimize resource allocation and process costs to reduce multi-process conflicts, including:

[0131] Process ;in, This represents the sensitivity coefficient of resources to complexity. Indicates the total number of resources;

[0132] Process Process cost ;in, Indicates the cost coefficient;

[0133] when At that time, stop optimizing resource allocation and process costs.

[0134] The present invention also provides an electronic device, including a processor, a communication module connected to the processor, and a memory, the electronic device being used to implement the integrated composite processing technology planning method for irregularly shaped tubes.

[0135] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0137] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the principles described, the implementation of the present invention may have any changes or modifications.

Claims

1. A method for planning an integrated composite processing technology for irregularly shaped tubes, characterized in that, The method includes: Obtain the technical information of the target product, and import the pre-set composite processing chain of the target product based on the technical information; Optimize the complex machining process chain to reduce multi-process conflicts and decrease cumulative process errors; including: The physical coupling chains between multiple processes in a quantitative composite machining process chain are decoupled using decoupling strategies to reduce multi-process conflicts. Specifically, this includes: Assume a complex processing chain includes processes ;in Indicates the number of processes; Each process outputs a state vector: ; ;in, Represents the residual stress tensor; Indicates cumulative plastic strain; Represents the temperature field; Represents the geometric deviation vector; State transfer equation ; Indicates the first The physical evolution function of each process step; Indicates the first Control inputs for each process; Indicates random noise; The physical coupling chain is then quantized as follows: ; Sensing process status, including: Establish observation equations ;in, Represents the observation matrix; Indicates measurement noise; Represents sensor data; Predict the process status and update the process status using Kalman filtering: ; where the Kalman gain matrix ; Indicates the sensor noise covariance; This represents the predicted state value at the previous time step. Represents the state prediction covariance matrix; Decouple the physical coupling chain between processes, i.e. perform feedforward compensation to cancel the interference from the previous process, so that the output state vector of each process is close to the actual state vector. Construct compensator To adjust the control input; Decoupling function ;in, Represents the decoupling gain matrix. Indicates the stress attenuation memory factor; This indicates the penalty coefficient for exceeding temperature limits; Represents the geometric deviation projection matrix; Indicate process The residual stress value; Indicates a safe temperature field; Insert a reset factor to reset the state: ;in, Indicates the stress relief rate; Represents the geometric correction matrix; Indicates the reset operator; Calculate the rate of decrease in coupling. ;in This represents the final output state vector of the decoupled composite processing chain. Represents the ideal output state vector; This represents the final output state vector of the composite processing chain when decoupling is not performed; if Therefore, it is determined that after decoupling, the interference of the previous process on the current process state is reduced; After the physical coupling chain is decoupled, error control and time optimization of the composite machining process chain are balanced through error accumulation suppression and dynamic precision reset strategies.

2. The integrated composite processing technology planning method for irregularly shaped tubes according to claim 1, characterized in that, The process chain for importing the target product's pre-set composite processing steps based on technical information includes: Product cross-sectional features, curvature features, and wall thickness features are extracted from product technical information to form a product complexity dataset. Product dimensional tolerances and geometric tolerances are obtained from product technical information to form a product accuracy dataset; The product's yield strength and ductility are obtained from the product's technical information to form a material property dataset; Based on the product's technical information, determine whether it is an irregularly shaped tube. If so, import the preset composite processing chain; otherwise, trigger a data anomaly alarm and reacquire the technical information of the target product.

3. The integrated composite processing technology planning method for irregularly shaped tubes according to claim 2, characterized in that, After the physical coupling chain is decoupled, the error control and time optimization of the composite machining process chain are balanced through error accumulation suppression and dynamic accuracy reset strategies, including: Construct error accumulation suppression and dynamic accuracy reset strategies; Perform reset requirement prediction, and determine whether the reset conditions are met based on the error accumulation suppression and dynamic precision reset strategy. If so, perform reset; otherwise, do not reset. When performing a reset, the reset strength is adaptively adjusted. Time compensation is performed.

4. The integrated composite processing technology planning method for irregularly shaped tubes according to claim 3, characterized in that, The process of predicting reset requirements, based on a reset strategy that combines error accumulation suppression and dynamic precision, involves determining whether the reset conditions are met, including: Construct the error accumulation dynamic equation: Let the first The output error of each process is ,in , Indicates the first Error propagation matrix of each process step; Indicates the first Process control input matrix, Indicates random perturbation; Defined in The decision variable for resetting the process step is: ;in, This represents the reset demand index; Indicates the dynamic decision threshold; Predicting the error sensitivity of subsequent processes ;in, Indicates the current error. Indicates future risks; Represents the error weight matrix; error accumulation and propagation operator. , Indicates the criticality of process quality. ; , Indicates the balance coefficient; Indicates the first j Error propagation matrix of each process step; Dynamic decision threshold ;in, Indicates the reset time; Indicate process Standard beat; , Indicates the adjustment parameter; Indicates the cumulative number of resets; The error accumulation suppression and dynamic accuracy reset strategy includes: Get Value, if Then in the process A reset station is added at the location for reset; if If not, then no reset will be performed.

5. The integrated composite processing technology planning method for irregularly shaped tubes according to claim 4, characterized in that, When performing a reset, the reset strength is adaptively adjusted, including: when At that time, for the first The output error of each process is reset; The error after reset, i.e., the reset strength. ; Among them, the adaptive reset matrix ; This represents the Sigmoid function. , Indicates the error limit threshold. Indicates the width of the transition interval.

6. The integrated composite processing technology planning method for irregularly shaped tubes according to claim 5, characterized in that, The time compensation includes: if Start accelerated measurement: Calculate compensation time ;in, Indicates the reset duration. Indicate process The buffer time between the next process; Indicates the buffer extraction coefficient; Record the compensation time and the process The buffer time between the next process and the next process is adjusted to This is to compensate for the time consumed by the reset station.

7. The integrated composite processing technology planning method for irregularly shaped tubes according to claim 6, characterized in that, Also includes: Based on process complexity, differentiate between irregularly shaped and non-irregularly shaped tubes, and optimize process resource allocation and process costs, including: Quantifying process complexity: process complexity ;in, , , Represents the shape complexity coefficient, process complexity coefficient, and material complexity coefficient; Shape complexity ; This represents the actual perimeter of the cross-section. Minimum circumference of the circumcircle Indicates the rate of change of cross-sectional area. Indicates the average cross-sectional area; Irregularly shaped tubes and non-irregularly shaped tubes are distinguished based on their shape complexity, i.e., when If the shape is irregular, it is determined to be an irregularly shaped tube; otherwise, it is determined to be a non-irregularly shaped tube. Process complexity ,in, ; Indicates the weight of device type; Material complexity ;in, Indicates the elastic modulus. Indicates the yield strength of the material. Indicates the density of the material. Indicates the melting point of the material; Based on process complexity, optimize resource allocation and process costs, namely: Process The allocated resources ;in, This represents the sensitivity coefficient of resources to complexity. Indicates the total number of resources; Process Process cost ;in, Indicates the cost coefficient; when At that time, stop optimizing resource allocation and process costs.

8. An electronic device, comprising a processor, a communication module connected to the processor, and a memory, characterized in that, The electronic device is used to implement the integrated composite processing planning method for irregularly shaped tubes as described in any one of claims 1-7.

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