Self-adaptive control system and method for complex structural part machining process

Abstract

The invention discloses a self-adaptive control system and method for a complex structural part machining process. The system comprises a stable parameter offline planning module, a machine tool numerical control system transformation module, a maximum threshold learning and storage module and a machining parameter self-adaptive control module. According to the method, the driving program is implanted into the numerical control system of the machine tool, the driving program instruction is added into the original G code program, the processing program is subjected to fragment division, and data acquisition and processing parameter target setting in the processing process are performed according to the divided program fragments; and regulating and controlling the machining parameters of each program fragment through a fuzzy control method, so that the self-adaptive regulation and control of the machining parameters in the overall machining process of the complex structural part are realized. And important practical significance is achieved for improving the intelligentization of the machining process of the complex structural part, improving the machining efficiency of the complex structural part and guaranteeing the machining quality of the complex structural part.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an adaptive control system and method for a complex structural part processing process. Background Art

[0002] With the development of modern industry, the demand for machining complex structural parts is increasing, especially in the fields of aviation, aerospace, automobiles, and precision machinery. These complex structural parts often have complex structures, high machining precision, and diverse materials, which poses great challenges to traditional machining technologies.

[0003] Most existing machining methods rely on fixed machining parameters and preset machining paths, and are unable to adapt to real-time changes during the machining process. In existing machining processes, technicians determine machining parameters such as cutting depth, spindle speed, and feed rate in advance through simulation or previous machining experience. Before starting machining, these parameters are input into the G-code machining program. During the machining process, the CNC machine tool processes complex structural parts according to the compiled G-code program. However, during the machining process, due to the influence of various factors such as material hardness, cutting force, and cutting temperature, cutting parameters (such as feed rate and spindle speed) need to be continuously adjusted to maintain the optimal machining effect. Traditional machining methods are often unable to obtain changes in these parameters in real time, resulting in low machining efficiency and unstable machining quality. Therefore, monitoring the machining process of complex structural parts and adaptively controlling the machining parameters during the machining process are of great significance to realizing intelligent manufacturing, improving the machining efficiency of complex structural parts, and ensuring machining quality.

[0004] Some existing machining parameter control technologies have, to a certain extent, achieved intelligent control of machining parameters during the machining process. In the patent "A Method for Adaptive Control of Process Parameters of CNC Machine Tools Taking Tool State into Account" (Application Number: CN202310077644.X), tool state prediction and spindle power prediction models are constructed separately. On this basis, a process parameter adaptive control model taking tool state into account is established, thereby achieving adaptive control of machining parameters. In the patent "A Method for Optimizing Adaptive Fuzzy Control Rules for CNC Machining Parameters" (Application Number: CN201310081486.1), the power bond graph method is used to optimize the fuzzy control rules for CNC machining adaptive control, improving control performance and stability. In the patent "A Method for Adaptive Control of Machine Tools Based on a GA-BP Neural Network Algorithm" (Application Number: CN201910732917.3), a neural network algorithm is used to perform real-time optimization and adaptive adjustment of feed rate and spindle speed, effectively improving machining efficiency and quality.

[0005] Through the analysis of processing parameter control technology, it is found that: (1) the existing processing parameter control technology constructs a variety of neural network models, and a large number of cross-experiments are required to improve the accuracy of the model. It is not suitable for complex structural parts with high added value and long processing time in the aerospace field; (2) the existing processing parameter control technology is mainly applicable to the processing process in which the initial processing parameters remain unchanged. The parameter control target in the processing process is single, and the scenario in which the setting processing parameters of the original CNC program change with the processing characteristics during the processing of complex parts is not considered. The processing parameter target cannot change with the change of processing characteristics, resulting in the existing processing parameter control technology cannot be applied to the processing process of complex parts in which the setting processing parameters change with the processing characteristics. Summary of the Invention

[0006] To address the problems of existing machining parameter control technologies, such as a single control target, an inability to adapt machining parameter targets to changes in machining characteristics, and a lack of suitability for complex structural parts with high added value and long machining times in the aerospace industry, the present invention proposes an adaptive control system and method for machining complex structural parts. This system embeds a driver into the machine tool's CNC system and adds driver instructions to the original G-code program. The machining program is segmented, and machining process data is collected and machining parameter targets are set according to the segmented program segments. Fuzzy control is used to adjust the machining parameters of each program segment, thereby achieving adaptive control of machining parameters during the overall machining process of complex structural parts. This method has important practical significance for improving the intelligence of the machining process of complex structural parts, enhancing the machining efficiency of complex structural parts, and ensuring the machining quality of complex structural parts.

[0007] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0008] In a first aspect, the present invention discloses an adaptive control system for the processing of complex structural parts, the system comprising a stable parameter offline planning module, a machine tool numerical control system transformation module, a maximum threshold learning and storage module, and a processing parameter adaptive control module;

[0009] The stable parameter offline planning module is used to build an offline simulation model of stable machining parameters for complex structural parts, simulate the machining process of complex structural parts based on the constant cutting force principle, and write the original G-code program according to the stable machining parameters set by the simulation;

[0010] The machine tool CNC system transformation module is used to implant a driver program in the machine tool CNC system and define the R parameters of the CNC system; wherein the implanted driver program is used to implement segmentation of the original G code program during the machining process, selection and activation of controlled machining parameters, and on / off control of the adaptive control function; the R parameters are used to store initial machining parameters and establish a connection between the machine tool CNC system, the driver program, and the original G code program;

[0011] The maximum threshold learning and storage module uses a driver embedded in the machine tool numerical control system to segment the original G-code program, collects data of the machining process according to the segmented program segments, extracts the maximum peak value of the signal corresponding to each segment after segmentation, and stores the corresponding maximum peak value of the machining process in a database according to the program name and the segment number after segmentation;

[0012] The processing parameter adaptive control module uses a driver embedded in the machine tool CNC system to segment the original G-code program, select control parameters, activate the adaptive control function, retrieve the corresponding maximum peak value stored in the database according to the program name and the segment number, perform data collection of the processing process, calculate and compare the relationship between the real-time collected signal and the maximum peak value, and perform adaptive control of the processing parameters based on the comparison result until the processing program is completed.

[0013] Furthermore, the stable parameter offline planning module includes a model building unit, a material property definition unit, a model importing unit, an initial cutting parameter setting unit, a simulation calculation unit, a processing parameter determination unit and a G code writing unit;

[0014] The model building unit is used to create a three-dimensional geometric model of the complex structural part to be processed and a geometric model of a tool used in actual processing;

[0015] The material property definition unit defines the material properties of the complex structural part and the cutting tool respectively;

[0016] The model import unit imports the created geometric model of the complex structural part and the tool into the ABAQUS finite element analysis software, meshes the model, and sets boundary conditions according to the actual processing conditions of the complex structural part;

[0017] The initial cutting parameter setting unit determines the processing parameters including the initial cutting speed, feed rate and cutting depth for the complex structural part to be processed based on previous processing experience or process manuals, and determines the target cutting force based on the constant cutting force principle;

[0018] The simulation calculation unit starts the process simulation module of the ABAQUS finite element analysis software to perform simulation calculation of the cutting process and solve the physical quantities of stress, strain and cutting force in the cutting process;

[0019] The machining parameter determination unit optimizes and adjusts the cutting parameters using a genetic algorithm based on the change in cutting force, so that the cutting force gradually approaches the target cutting force. When the cutting force is within a preset fluctuation range relative to the target cutting force, the cutting parameter at the corresponding moment is output as a stable machining parameter.

[0020] The G code writing unit writes an original G code program according to the stable processing parameters output by the processing parameter determination unit.

[0021] Furthermore, the machine tool CNC system transformation module implants a driver into the user cycle column under the NC program column of the machine tool CNC system. The implanted driver includes two function programs, ADACTRL (a, b, c) and ADAOFF. ADACTRL (a, b, c) indicates the beginning of a new segment, and ADAOFF indicates the end of the adaptive control process. In ADACTRL (a, b, c), a indicates whether feed adaptation is turned on, wherein a value of 0 indicates that feed adaptation is turned off, and a value of 1 indicates that feed adaptation is turned on; b indicates whether spindle speed adaptation is turned on, wherein b value of 0 indicates that spindle speed adaptation is turned off, and b value of 1 indicates that spindle speed adaptation is turned on; c indicates the divided segment number, and c is a natural number greater than or equal to 1.

[0022] Furthermore, the maximum threshold learning and storage module includes a segmentation unit, a processing data acquisition unit and a peak extraction unit;

[0023] The segmentation unit traverses the original G-code program and determines whether the initial processing parameters including cutting depth, feed rate and spindle speed have changed. When the initial processing parameters have changed, a driver function instruction is added to perform segment division;

[0024] The processing data acquisition unit collects data of the processing process according to the divided program segments, and performs noise reduction and filtering on the collected processing data;

[0025] The peak value extraction unit extracts the maximum peak value of the signal corresponding to each divided program segment, and stores the maximum peak value of the corresponding processing process in the database according to the program name and the divided segment number.

[0026] Furthermore, the processing parameter adaptive control module includes a program modification unit, a processing control unit, a peak value comparison unit and an adaptive control unit;

[0027] The program modification unit uses the driver function instructions embedded in the machine tool numerical control system to segment the original G code program, select control parameters, and activate the adaptive control function;

[0028] The processing control unit controls the CNC system of the machine tool to run the modified G code program to process the complex structural parts;

[0029] The peak comparison unit retrieves the maximum peak value corresponding to the control parameter stored in the database according to the program name and the divided segment number, and simultaneously collects the real-time data of the control parameter during the processing, and calculates and compares the relationship between the real-time data of the control parameter and the maximum peak value of the control parameter in the database;

[0030] The adaptive control unit uses a fuzzy control method to adaptively adjust the control parameters based on the comparison result output by the peak comparison unit, so that the control parameters collected in real time approach their corresponding maximum peak values ​​until the processing program is completed and the processing control unit is called to terminate the processing process.

[0031] Furthermore, the processing parameter adaptive control module and the maximum threshold learning and storage module maintain consistency in the division positions and number of segments of the original G-code program segments.

[0032] Furthermore, the adaptive control unit multiplies the corresponding maximum peak value retrieved from the database by a coefficient and then compares it with the real-time data of the control parameter.

[0033] In a second aspect, the present invention discloses an adaptive control method for a complex structural part processing process, characterized in that the method is executed based on the system described above; the method comprises the following steps:

[0034] S1, build an offline simulation model of stable machining parameters for complex structural parts, simulate the machining process of complex structural parts based on the principle of constant cutting force, and write the original G-code program according to the stable machining parameters set by the simulation;

[0035] S2, implanting a driver program in the CNC system of the machine tool and defining the R parameters of the CNC system; wherein the implanted driver program is used to implement segmentation of the original G code program during the machining process, selection and activation of the controlled machining parameters, and on / off control of the adaptive control function; the R parameters are used to store the initial machining parameters and establish a connection between the CNC system of the machine tool, the driver program, and the original G code program;

[0036] S3, using the driver embedded in the CNC system of the machine tool to segment the original G-code program, collecting data of the machining process according to the segmented program segments, extracting the maximum peak value of the signal corresponding to each segment after segmentation, and storing the corresponding maximum peak value of the machining process in a database according to the program name and the segment number after segmentation;

[0037] S4, using the driver embedded in the CNC system of the machine tool to segment the original G-code program, select the control parameters, activate the adaptive control function, retrieve the corresponding maximum peak value stored in the database according to the program name and the segment number, perform data acquisition of the processing process, calculate and compare the relationship between the real-time collected signal and the maximum peak value, and perform adaptive control of the processing parameters based on the comparison result until the processing program is completed.

[0038] Step S1 further comprises:

[0039] S11, using UG software to create 3D geometric models of complex structural parts for outsourcing processing;

[0040] S12, establishing a geometric model of the tool according to the type of tool used in actual processing, including cutting edge shape, tool diameter, tool length and geometric parameters of the cutting edge;

[0041] S13, defining the material properties of the complex structural parts to be processed and the tool, including elastic model, Poisson's ratio, density, and yield strength;

[0042] S14, importing the created geometric model of the complex structural part and tool to be processed into ABAQUS finite element analysis software and performing meshing on it;

[0043] S15, setting boundary conditions based on the actual processing conditions of complex structural parts. In the finite element analysis software, displacement constraints are set to simulate the clamping and positioning during the actual processing. At the same time, the contact conditions between the tool and the complex structural part to be processed are defined, including the contact type and friction coefficient settings.

[0044] S16, based on previous processing experience or process manuals, determine the initial cutting speed, feed rate and cutting depth processing parameters for processing the complex structural part to be processed, and set them in the finite element analysis software;

[0045] S17, according to the principle of constant cutting force, determine the target cutting force. The calculation formula of cutting force is as follows:

[0046]

[0047] Where, is the cutting force coefficient; a p is the cutting depth; is the cutting depth a p The index of; f is the feed rate; is the index of feed rate f; v is cutting speed; is the exponent of cutting speed v;

[0048] S18, starting the process simulation module of ABAQUS finite element analysis software, performing simulation calculation of the cutting process, and solving the physical quantities of stress, strain and cutting force in the cutting process;

[0049] S19, according to the change of cutting force, the cutting parameters are optimized and adjusted through the genetic algorithm so that the cutting force gradually approaches the target cutting force. When the fluctuation range of the cutting force relative to the target cutting force is within ±5%, the cutting parameters at this time are used as stable processing parameters.

[0050] Furthermore, in step S4, the process of adaptively adjusting the control parameters using the fuzzy control method so that the control parameters collected in real time approach their corresponding maximum peak values ​​includes the following steps:

[0051] S41, obtaining the spindle power signal in real time during the machining process based on the power sensor;

[0052] S42, solve the difference ΔE between the real-time collected spindle power signal and the maximum peak threshold and the power signal difference change rate

[0053] S43, for ΔE, And the output control quantity U is fuzzyized;

[0054] S44, determining fuzzy control rules;

[0055] S45, perform fuzzy synthesis operation to obtain the fuzzy relationship matrix R:

[0056] R = U(E × EI) × U;

[0057] In the formula, × is the membership operation symbol in fuzzy mathematics theory;

[0058] According to the membership table of each change level of E and EI, the corresponding U is obtained according to the control rule, and then the corresponding control table is obtained through weighted average calculation;

[0059] S46, after input E and EI are given, the corresponding output controlled variable U is obtained by querying the control table and performing weighted average calculation;

[0060] S47, defuzzifying the output controlled variable U by multiplying it with a given quantization factor, and outputting the change of the controlled variable during the complex part processing process.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] First, the adaptive control system and method for the processing of complex structural parts of the present invention can realize real-time monitoring and adaptive adjustment of process parameters during the processing of complex structural parts. It has good applicability and high controllability. At the same time, it can provide a visual processing parameter change curve, which is helpful to understand the real-time processing process of complex structural parts, optimize processing efficiency, and record processing process data, which is helpful for process information statistics and process retrospective analysis.

[0063] Second, the adaptive control system and method for the processing of complex structural parts of the present invention realizes adaptive control of process parameters in the processing of complex structural parts by implanting a driver in the CNC system of the machine tool and adding driver instructions to the original G-code program, dividing the processing program into segments, and collecting processing data and setting processing parameter targets according to the divided program segments, thereby realizing adaptive control of process parameters in the processing of complex structural parts.

[0064] Third, the adaptive control system and method for the processing of complex structural parts of the present invention realizes the control of process parameters in the processing of complex structural parts through fuzzy control methods. Compared with traditional processing methods, this method is more scientific and reasonable, and can greatly improve processing efficiency and give full play to the performance of machine tools. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of the structure principle of the adaptive control system for complex structural parts processing of the present invention;

[0066] Figure 2 A flow chart showing the stable machining parameters obtained through pre-simulation by software;

[0067] Figure 3 A schematic diagram of segmenting the original G-code program in the maximum threshold learning and storage module during the actual machining process of a part;

[0068] Figure 4 This is a schematic diagram of segmenting the original G-code program in the machining parameter adaptive control module during the actual machining process of a certain part.

[0069] Figure 5 This is the fuzzy control flow chart of complex parts processing parameters in the processing parameter adaptive control module. DETAILED DESCRIPTION

[0070] The embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0071] Example 1

[0072] like Figure 1As shown, the adaptive control system for complex structural parts processing in this embodiment includes a stable parameter offline planning module, a machine tool CNC system transformation module, a maximum threshold learning and storage module, and a processing parameter adaptive control module.

[0073] In an adaptive control system for complex structural part machining, the stable parameter offline planning module constructs an offline simulation model for stable machining parameters based on the machining characteristics, process requirements, material information, and tool information of the complex structural part. This model then simulates the machining process of the complex structural part based on the principle of constant cutting force, thereby obtaining stable machining parameters set through simulation. When the machining process for the workpiece is mature, initial stable machining parameters can be set based on previous machining experience and machine tool performance.

[0074] Specifically, when machining a new complex structural part, it is necessary to build an offline simulation model of the stable machining parameters of the complex structural part, and simulate the machining process of the complex structural part based on the principle of constant cutting force, so as to obtain the stable machining parameters set by simulation. Figure 2 The specific steps are as follows:

[0075] (1) Establish the geometric model of the workpiece to be processed: Use UG software to accurately create a three-dimensional geometric model of the complex structural part, ensuring that the size, shape and features (holes, slots, curved surfaces) of the model are consistent with the actual workpiece. Pay attention to the integrity of the model during the modeling process to avoid collective defects such as missing surfaces and overlapping surfaces.

[0076] (2) Cutting tool modeling: According to the type of tool used in actual processing, the geometric model of the tool is established, which mainly includes the cutting edge shape, tool diameter, tool length and geometric parameters of the cutting edge.

[0077] (3) Material property definition: Define the material properties of the workpiece to be processed and the tool respectively, mainly including elastic model, Poisson's ratio, density, yield strength, etc.

[0078] (4) Model import and meshing: Import the created geometric model of the workpiece and tool to be processed into the ABAQUS finite element analysis software and mesh it. During the meshing process, special attention should be paid to using finer meshes in areas where stress changes drastically (such as near the processing surface, corners, etc.).

[0079] (5) Determine boundary conditions and loads: Set boundary conditions according to the actual processing conditions of complex structural parts. In the finite element analysis software, set displacement constraints to simulate the clamping and positioning in the actual processing process. At the same time, define the contact conditions between the tool and the complex structural parts to be processed, including the contact type and friction coefficient settings.

[0080] (6) Initial cutting parameter setting: Based on previous processing experience or process manuals, determine the initial cutting speed, feed rate, cutting depth and other processing parameters for the complex structural parts to be processed, and set them in the finite element analysis software.

[0081] (7) Define the target cutting force value: According to the principle of constant cutting force, determine the target cutting force. The calculation formula of cutting force is as follows:

[0082]

[0083] Where, is the cutting force coefficient, which is related to many factors such as workpiece material, tool material and cutting conditions; a p is the cutting depth; is the cutting depth a p The index indicates the influence of cutting depth on cutting force, which is generally between 0.8 and 1.0; f is the feed rate; is the index of feed rate f, and its value is generally between 0.6 and 0.8, indicating that the influence of feed rate on cutting force is less than that of cutting depth; v is cutting speed; It is the index of cutting speed v, usually between -0.1 and 0.1, indicating that the effect of cutting speed on cutting force is relatively small.

[0084] (8) Perform finite element simulation calculation: After the parameter setting is completed, start the process simulation module of ABAQUS finite element analysis software to perform simulation calculation of the cutting process and solve physical quantities such as stress, strain and cutting force in the cutting process.

[0085] (9) Determination of stable machining parameters: According to the changes in cutting force, the cutting parameters are optimized and adjusted through genetic algorithms so that the cutting force gradually approaches the target cutting force. When the cutting force fluctuates slightly around the target cutting force (the fluctuation range is within ±5%), the cutting parameters at this time are stable machining parameters.

[0086] The machine tool CNC system modification module involves implanting a driver program into the machine tool's CNC system and defining the CNC system's R parameters. Specifically, the driver program is implanted in the "User Cycle" column under the "NC Program" column of the machine tool's CNC system. The implanted driver program is a custom function used to implement segmentation of the original G-code program during the machining process, the selection and activation of controlled machining parameters, and the on / off control of adaptive control functions. The implanted driver program defines R parameters to store machining parameters such as the initial feed rate and spindle speed. By adding the driver program's function instructions to the original G-code program, the connection between the machine tool's CNC system, the driver program, and the original G-code program is established.

[0087] For example, the embedded driver includes two functions: ADACTRL(a,b,c) and ADAOFF. ADACTRL(a,b,c) indicates the start of a new segment, while ADAOFF indicates the end of the adaptive control process. In ADACTRL(a,b,c), a indicates whether feed adaptation is enabled (0 for disabled and 1 for enabled); b indicates whether spindle speed adaptation is enabled (0 for disabled and 1 for enabled); and c indicates the segment number (c = 1, 2, 3, 4, 5, etc.), starting from 1 and increasing in sequence.

[0088] like Figure 3 As shown, box 2 is the R parameter instruction added in the original G code program, which assigns R188=1000, thereby transmitting the original processing parameters set in the G code to the driver embedded in the CNC system.

[0089] In the maximum threshold learning and storage module, the original G-code program is segmented using the driver function instructions implanted in the CNC system. Data of the machining process is collected according to the segmented program segments, and the collected signals are subjected to noise reduction, filtering, and feature extraction. The maximum peak value of the signal corresponding to each segmented program segment is extracted, and the corresponding maximum peak value of the machining process is stored in the database according to the program name and the segment name after segmentation.

[0090] like Figure 3 As shown, blocks 1, 3, and 4 implement segmentation of the original G-code program. Parameters a and b are both 0, indicating that the adaptive control function of the machining parameters is not activated and is in a learning state. c is 1 and 2, respectively, indicating that the original G-code machining program is divided into segments 1 and 2. Block 5 indicates the end of segment segmentation. Data from the machining process is collected based on the segmented program segments. The collected signals are subjected to noise reduction, filtering, and feature extraction. The maximum peak value of the signal corresponding to each segmented program segment is extracted and stored in a database according to the program name and the segment name.

[0091] Preferably, the basis for adding a driver function implanted in the CNC system in the original G code program for segment division is whether the initial processing parameters such as cutting depth, feed speed and spindle speed change. If the initial processing parameters change, a driver function instruction is added to perform segment division.

[0092] In the machining parameter adaptive control module, the original G code program is segmented using the driver function instructions implanted in the CNC system, the control parameters are selected, and the control switches are activated, such as Figure 4As shown, blocks 1, 3, and 4 implement the segmentation of the program. Parameter a in the ADACTRL(a, b, c) instructions in blocks 3 and 4 is 1, indicating that the selected control parameter is feed rate and the control switch is activated. c values ​​of 1 and 2, respectively, indicate that the original G-code program has been divided into segments 1 and 2. Block 5 indicates the completion of the segmentation process. Block 2 is used to connect the initial machining parameters in the G-code program with the driver program embedded in the CNC system. Based on the program name and segment number, the corresponding maximum peak value stored in the database is retrieved for data acquisition during the machining process. The relationship between the real-time acquired signal and the maximum peak value is calculated and compared, and adaptive control of the machining parameters is performed based on this information until the machining program is completed.

[0093] In this embodiment, in the processing parameter adaptive control module, the division position and number of the original G code program segments are consistent with the division position and number of the G code program segments in the corresponding maximum threshold learning and storage module. Figure 3 and Figure 4 As shown in Figure 2, the fragment division positions and the number of fragments of the two programs are consistent.

[0094] Example 2

[0095] like Figure 1 As shown, this embodiment discloses an adaptive control method for the processing of complex structural parts, which specifically includes the following steps:

[0096] S1, build an offline simulation model of stable machining parameters for complex structural parts, simulate the machining process of complex structural parts based on the principle of constant cutting force, and obtain stable machining parameters.

[0097] S2, write the original G-code machining program based on the stable machining parameters obtained by simulation.

[0098] S3, compile a driver program for realizing segmentation of the original G code program in the machining process, selection and activation of the controlled machining parameters, and switch control of the adaptive control function, and implant it into the CNC system of the machine tool.

[0099] S4, defines the R parameter, which is used to store the initial processing parameters such as feed rate and spindle speed.

[0100] S5, modifying the compiled original G code program and dividing the original G code program into segments.

[0101] S6, runs the modified G code program and collects the spindle power signal in real time during the machining process.

[0102] S7: Perform noise reduction and filtering on the collected signal, extracting peak features from each segment according to the segmented program, thereby obtaining a maximum peak threshold for each segment. Preferably, step S7 can be repeated multiple times for the same machining process, by repeatedly collecting process signals from the same segment of the same machining process, followed by noise reduction, filtering, and feature extraction, to obtain a more accurate and adaptable maximum peak threshold.

[0103] S8, storing the extracted maximum peak threshold in a database according to the program name and the divided segment name.

[0104] S9, uses the driver function instruction implanted in the CNC system to segment the original G code program, selects the controlled processing parameters, and activates the control switch.

[0105] S10, start processing, and collect the spindle power signal in real time during the processing.

[0106] S11, automatically calling the corresponding maximum peak threshold stored in the database according to the program name and the divided segment number.

[0107] S12, calculates and compares the real-time collected machining process signal with the corresponding maximum peak threshold retrieved from the database, and adjusts the machining parameters in real time based on the fuzzy control method, so that the real-time monitoring signal approaches the corresponding maximum peak threshold, ensuring that the real-time monitoring signal fluctuates near the maximum peak threshold. For example, the machining parameter controlled in step S12 can be the spindle speed or feed rate, and the fuzzy control method is used to adaptively control the machining parameters to ensure that the real-time monitoring signal fluctuates near the maximum peak threshold. Preferably, a coefficient can be multiplied for the corresponding peak threshold feature retrieved from the database to appropriately increase the peak threshold to maximize the performance of the machine tool.

[0108] S13, step S12 continues until the processing program is completed.

[0109] like Figure 5 As shown, the fuzzy control of feed rate in the machining parameter adaptive control module in step S12 includes the following steps:

[0110] S1201: obtaining the spindle power signal during machining in real time based on the power sensor;

[0111] S1202: Calculate the difference between the real-time collected spindle power signal and the maximum peak threshold (spindle power signal difference) ΔE and the power signal difference change rate

[0112] S1203: ΔE, And the output control quantity U is fuzzyized;

[0113] The variation range of the monitored spindle power signal difference is divided into 14 levels, namely {-6, -5, -4, -3, -2, -1, -0, +0, 1, 2, 3, 4, 5, 6}, and the variation range of the spindle power signal difference is divided into 8 fuzzy sets, namely negative large (NB), negative medium (NM), negative small (NS), negative zero (NO), positive zero (PO), positive small (PS), positive medium (PM), and positive large (PB). The obtained fuzzy table of spindle power signal difference is shown in Table 1.

[0114] Table 1

[0115]

[0116] Similarly, the change rate of the spindle power signal difference is divided into 13 levels: {-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6}, and the change rate of the spindle power signal difference is divided into 7 fuzzy sets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB). The fuzzy table of the spindle power signal difference change rate is shown in Table 2.

[0117] Table 2

[0118]

[0119]

[0120] Similarly, "U" is used to represent the variable of "output control quantity", which is divided into 15 change levels {-7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7}, and is divided into 7 fuzzy sets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB). The fuzzy table of the output change quantity is shown in Table 3.

[0121] Table 3

[0122]

[0123] S1204: Determine fuzzy control rules;

[0124] The fuzzy control rules of the present invention are described as follows using fuzzy conditional statements:

[0125] (1)if(E=NB or NM)and(EI=NB or NM)then U=PB

[0126] (2)if(E=NB or NM)and(EI=NS or ZO)then U=PB

[0127] (3)if(E=NB or NM)and(EI=PS)then U=PM

[0128] (4)if(E=NB or NM)and(EI=PM or PB)then U=ZO

[0129] (5)if(E=NS)and(EI=NB or NM)then U=PM

[0130] (6)if(E=NS)and(EI=NS or ZO)then U=PM

[0131] (7)if(E=NS)and(EI=PS)then U=ZO

[0132] (8)if(E=NS)and(EI=PM or PB)then U=NS

[0133] (9)if(E=NO or PO)and(EI=NB or NM)then U=PM

[0134] (10)if(E=NO or PO)and(EI=NS)then U=PS

[0135] (11)if(E=NO or PO)and(EI=ZO)then U=ZO

[0136] (12)if(E=NO or PO)and(EI=PS)then U=NS

[0137] (13)if(E=NO or PO)and(EI=PM or PB)then U=NM

[0138] (14)if(E=PS)and(EI=NB or NM)then U=PS

[0139] (15)if(E=PS)and(EI=NS)then U=ZO

[0140] (16)if(E=PS)and(EI=ZO or PS)then U=NM

[0141] (17)if(E=PS)and(EI=PM or PB)then U=NM

[0142] (18)if(E=PM or PB)and(EI=NB or NM)then U=ZO

[0143] (19)if(E=PM or PB)and(EI=NS)then U=NM

[0144] (20)if(E=PM or PB)and(EI=ZO or PS)then U=NB

[0145] (21)if(E=PM or PB)and(EI=PM or PB)then U=NB

[0146] The above fuzzy conditional statements can be summarized as the fuzzy control rules shown in Table 4.

[0147] Table 4

[0148]

[0149] S1205: Perform fuzzy synthesis operation, calculate the fuzzy relationship matrix, and generate a control table;

[0150] According to the fuzzy rules, the fuzzy relation R can be obtained:

[0151] R=U(E×EI)×U

[0152] Where × is the membership operation symbol in fuzzy mathematics theory.

[0153] After the fuzzy relationship R is obtained, the corresponding U is obtained according to the membership table of each change level of E and EI according to the control rule, and then the corresponding control table is obtained through weighted average calculation (see Table 5).

[0154] Table 5

[0155]

[0156]

[0157] S1206: Perform fuzzy judgment on the output according to the given input; after the inputs E and EI are given, the corresponding output controlled quantity U is obtained by querying the control table and performing weighted average calculation.

[0158] S1207: Defuzzify the output controlled variable U through a multiplication operation with a given quantization factor, and output the change of the controlled variable during the complex part processing process.

[0159] The fuzzy control method of the spindle speed adopted in the machining parameter adaptive control module in step S12 is the same as the fuzzy control method of the feed rate.

[0160] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0161] 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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.

[0162] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0163] 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 the instructions for executing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0164] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0165] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. An adaptive control system for complex structural parts processing, characterized by: The system includes a stable parameter offline planning module, a machine tool numerical control system transformation module, a maximum threshold learning and storage module and a processing parameter adaptive control module; The stable parameter offline planning module is used to build an offline simulation model of stable machining parameters for complex structural parts, simulate the machining process of complex structural parts based on the constant cutting force principle, and write the original G-code program according to the stable machining parameters set by the simulation; The machine tool CNC system transformation module is used to implant a driver program in the machine tool CNC system and define the R parameters of the CNC system; wherein the implanted driver program is used to implement segmentation of the original G code program during the machining process, selection and activation of controlled machining parameters, and on / off control of the adaptive control function; the R parameters are used to store initial machining parameters and establish a connection between the machine tool CNC system, the driver program, and the original G code program; The maximum threshold learning and storage module uses a driver embedded in the machine tool numerical control system to segment the original G-code program, collects data of the machining process according to the segmented program segments, extracts the maximum peak value of the signal corresponding to each segment after segmentation, and stores the corresponding maximum peak value of the machining process in a database according to the program name and the segment number after segmentation; The processing parameter adaptive control module uses a driver embedded in the machine tool CNC system to segment the original G-code program, select control parameters, activate the adaptive control function, retrieve the corresponding maximum peak value stored in the database according to the program name and the segment number, perform data collection of the processing process, calculate and compare the relationship between the real-time collected signal and the maximum peak value, and perform adaptive control of the processing parameters based on the comparison result until the processing program is completed.

2. The adaptive control system for complex structural parts processing according to claim 1, characterized in that: The stable parameter offline planning module includes a model construction unit, a material property definition unit, a model import unit, an initial cutting parameter setting unit, a simulation calculation unit, a processing parameter determination unit and a G code writing unit; The model building unit is used to create a three-dimensional geometric model of the complex structural part to be processed and a geometric model of the tool used in actual processing; The material property definition unit defines the material properties of the complex structural part and the cutting tool respectively; The model import unit imports the created geometric model of the complex structural part and the tool into the ABAQUS finite element analysis software, meshes the model, and sets boundary conditions according to the actual processing conditions of the complex structural part; The initial cutting parameter setting unit determines the processing parameters including the initial cutting speed, feed rate and cutting depth for the complex structural part to be processed based on previous processing experience or process manuals, and determines the target cutting force based on the constant cutting force principle; The simulation calculation unit starts the process simulation module of the ABAQUS finite element analysis software to perform simulation calculation of the cutting process and solve the physical quantities of stress, strain and cutting force in the cutting process; The machining parameter determination unit optimizes and adjusts the cutting parameters using a genetic algorithm based on the change in cutting force, so that the cutting force gradually approaches the target cutting force. When the cutting force is within a preset fluctuation range relative to the target cutting force, the cutting parameter at the corresponding moment is output as a stable machining parameter. The G code writing unit writes an original G code program according to the stable processing parameters output by the processing parameter determination unit.

3. The adaptive control system for complex structural parts processing according to claim 1, characterized in that: The machine tool numerical control system transformation module implants a driver into the user cycle column under the NC program column of the machine tool numerical control system. The implanted driver includes two function programs, ADACTRL (a, b, c) and ADAOFF. ADACTRL (a, b, c) indicates the beginning of a new segment, and ADAOFF indicates the end of the adaptive control process. In ADACTRL (a, b, c), a indicates whether feed adaptation is turned on, wherein a value of 0 indicates that feed adaptation is turned off, and a value of 1 indicates that feed adaptation is turned on; b indicates whether spindle speed adaptation is turned on, wherein b value of 0 indicates that spindle speed adaptation is turned off, and b value of 1 indicates that spindle speed adaptation is turned on; c indicates the divided segment number, and c is a natural number greater than or equal to 1.

4. The adaptive control system for complex structural parts processing according to claim 1, characterized in that: The maximum threshold learning and storage module includes a segmentation unit, a processing data acquisition unit and a peak extraction unit; The segmentation unit traverses the original G-code program and determines whether the initial processing parameters including cutting depth, feed rate and spindle speed have changed. When the initial processing parameters have changed, a driver function instruction is added to perform segment division; The processing data acquisition unit collects data of the processing process according to the divided program segments, and performs noise reduction and filtering on the collected processing data; The peak value extraction unit extracts the maximum peak value of the signal corresponding to each divided program segment, and stores the maximum peak value of the corresponding processing process in the database according to the program name and the divided segment number.

5. The adaptive control system for complex structural parts processing according to claim 1, characterized in that: The processing parameter adaptive control module includes a program modification unit, a processing control unit, a peak value comparison unit and an adaptive control unit; The program modification unit uses the driver function instructions embedded in the machine tool numerical control system to segment the original G code program, select control parameters, and activate the adaptive control function; The processing control unit controls the CNC system of the machine tool to run the modified G code program to process the complex structural parts; The peak comparison unit retrieves the maximum peak value corresponding to the control parameter stored in the database according to the program name and the divided segment number, and simultaneously collects the real-time data of the control parameter during the processing, and calculates and compares the relationship between the real-time data of the control parameter and the maximum peak value of the control parameter in the database; The adaptive control unit uses a fuzzy control method to adaptively adjust the control parameters based on the comparison result output by the peak comparison unit, so that the control parameters collected in real time approach their corresponding maximum peak values ​​until the processing program is completed and the processing control unit is called to terminate the processing process.

6. The adaptive control system for complex structural parts processing according to claim 1, characterized in that: The processing parameter adaptive control module and the maximum threshold learning and storage module keep consistent the division positions and the number of segments of the original G code program segments.

7. The adaptive control system for complex structural parts processing according to claim 1, characterized in that: The adaptive control unit multiplies the corresponding maximum peak value retrieved from the database by a coefficient and then compares it with the real-time data of the control parameter.

8. An adaptive control method for complex structural parts processing, characterized in that: The method is performed based on the system according to any one of claims 1 to 7; the method comprises the following steps: S1, build an offline simulation model of stable machining parameters for complex structural parts, simulate the machining process of complex structural parts based on the principle of constant cutting force, and write the original G-code program according to the stable machining parameters set by the simulation; S2, implanting a driver program in the CNC system of the machine tool and defining the R parameters of the CNC system; wherein the implanted driver program is used to implement segmentation of the original G code program during the machining process, selection and activation of the controlled machining parameters, and on / off control of the adaptive control function; the R parameters are used to store the initial machining parameters and establish a connection between the CNC system of the machine tool, the driver program, and the original G code program; S3, using the driver embedded in the CNC system of the machine tool to segment the original G-code program, collecting data of the machining process according to the segmented program segments, extracting the maximum peak value of the signal corresponding to each segment after segmentation, and storing the corresponding maximum peak value of the machining process in a database according to the program name and the segment number after segmentation; S4, using the driver embedded in the CNC system of the machine tool to segment the original G-code program, select the control parameters, activate the adaptive control function, retrieve the corresponding maximum peak value stored in the database according to the program name and the segment number, perform data acquisition of the processing process, calculate and compare the relationship between the real-time collected signal and the maximum peak value, and perform adaptive control of the processing parameters based on the comparison result until the processing program is completed.

9. The adaptive control method for complex structural parts processing according to claim 8, characterized in that: Step S1 further comprises: S11, using UG software to create 3D geometric models of complex structural parts for outsourcing processing; S12, establishing a geometric model of the tool according to the type of tool used in actual processing, including cutting edge shape, tool diameter, tool length and geometric parameters of the cutting edge; S13, defining the material properties of the complex structural parts to be processed and the tool, including elastic model, Poisson's ratio, density, and yield strength; S14, importing the created geometric model of the complex structural part and tool to be processed into ABAQUS finite element analysis software and performing meshing on it; S15, setting boundary conditions based on the actual processing conditions of complex structural parts. In the finite element analysis software, displacement constraints are set to simulate the clamping and positioning during the actual processing. At the same time, the contact conditions between the tool and the complex structural part to be processed are defined, including the contact type and friction coefficient settings. S16, based on previous processing experience or process manuals, determine the initial cutting speed, feed rate and cutting depth processing parameters for processing the complex structural part to be processed, and set them in the finite element analysis software; S17, according to the principle of constant cutting force, determine the target cutting force. The calculation formula of cutting force is as follows: Where, is the cutting force coefficient; a p is the cutting depth; is the cutting depth a p The index of; f is the feed rate; is the index of feed rate f; v is cutting speed; is the exponent of cutting speed v; S18, starting the process simulation module of ABAQUS finite element analysis software, performing simulation calculation of the cutting process, and solving the physical quantities of stress, strain and cutting force in the cutting process; S19, according to the change of cutting force, the cutting parameters are optimized and adjusted through the genetic algorithm so that the cutting force gradually approaches the target cutting force. When the fluctuation range of the cutting force relative to the target cutting force is within ±5%, the cutting parameters at this time are used as stable processing parameters.

10. The adaptive control method for complex structural parts processing according to claim 8, characterized in that: In step S4, the process of adaptively adjusting the control parameters using the fuzzy control method so that the real-time collected control parameters approach their corresponding maximum peak values ​​includes the following steps: S41, obtaining the spindle power signal during the machining process in real time based on the power sensor; S42, solve the difference ΔE between the real-time collected spindle power signal and the maximum peak threshold and the power signal difference change rate S43, for ΔE, And the output control quantity U is fuzzyized; S44, determining fuzzy control rules; S45, perform fuzzy synthesis operation to obtain the fuzzy relationship matrix R: R = U(E × EI) × U; In the formula, × is the membership operation symbol in fuzzy mathematics theory; According to the membership table of each change level of E and EI, the corresponding U is obtained according to the control rule, and then the corresponding control table is obtained through weighted average calculation; S46, after input E and EI are given, the corresponding output controlled variable U is obtained by querying the control table and performing weighted average calculation; S47, defuzzifying the output controlled variable U by multiplying it with a given quantization factor, and outputting the change of the controlled variable during the complex part processing process.

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