A processing information analysis optimization method for furniture manufacturing and related device

Abstract

The application discloses a processing information analysis optimization method for furniture manufacturing and a related device, and relates to the technical field of data processing. The method comprises the following steps: disassembling furniture model data to obtain part data of target furniture; performing processing deformation prediction based on initial processing technology information determined by the part data and a cutting temperature model to modify the initial processing technology information; performing processing equipment scheduling analysis based on target processing technology information; performing equipment state detection on target scheduling processing equipment based on the target processing technology information and response surface modeling; adjusting the target processing technology information based on the equipment state detection information; and performing furniture manufacturing simulation based on the adjusted target processing technology information to optimize the processing technology information, and controlling equipment to manufacture furniture according to the optimized target processing technology information. The application can guarantee the reliability of processing technology information analysis and make furniture manufacturing more close to ideal results.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a processing information analysis and optimization method and related devices for furniture manufacturing. Background Art

[0002] With rapid economic development, my country's furniture industry has grown steadily, and customer demands for furniture quality are becoming increasingly stringent. To ensure the quality of furniture manufacturing, accurate analysis of furniture manufacturing process information is necessary. Since furniture parts will deform to a certain extent during processing, to minimize the impact of this deformation, deformation information must be predicted in advance to adjust the initial processing information. Currently, this method primarily relies on data comparison by personnel, but this method relies heavily on the professional expertise of personnel, making it difficult to accurately predict deformation and resulting in insufficient reliability of the final processing information. Furthermore, current processing information analysis lacks consideration of the changing state of processing equipment. Over time, the state and performance of processing equipment will change. Failure to account for this changing state can compromise the accuracy and stability of furniture manufacturing, reducing quality and making it difficult to meet the demands of efficient and precise processing for modern furniture. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology. The present invention provides a processing information analysis and optimization method and related devices for furniture manufacturing, which can ensure the reliability of processing technology information analysis and make the manufacturing and processing of furniture closer to the ideal effect.

[0004] In order to solve the above technical problems, the present invention provides a processing information analysis and optimization method for furniture manufacturing, the method comprising:

[0005] Decompose the furniture model data of the target furniture in the furniture manufacturing plan to obtain the parts data of the target furniture;

[0006] Based on the initial machining process information determined by the part data and the cutting temperature model, the residual stress analysis is used to predict the machining deformation to obtain the machining deformation prediction information, and the initial machining process information is corrected based on the machining deformation prediction information to obtain the target machining process information;

[0007] Perform processing equipment scheduling analysis based on target processing technology information to obtain target scheduling processing equipment;

[0008] Based on the target processing technology information combined with response surface modeling, the equipment status detection of the target scheduling processing equipment is carried out to obtain the equipment status detection information;

[0009] Adjust the target machining process information based on the equipment state detection information, and obtain adjusted target machining process information;

[0010] Perform furniture manufacturing simulation based on the adjusted target machining process information, optimize the adjusted target machining process information based on the furniture manufacturing simulation result, obtain optimized target machining process information, and control the target scheduling machining equipment to perform furniture manufacturing according to the optimized target machining process information.

[0011] Optionally, the furniture model data of the target furniture in the furniture manufacturing scheme is disassembled to obtain part data of the target furniture, including:

[0012] Extract the three-dimensional model of the target furniture, disassemble the three-dimensional model to obtain a plurality of furniture parts, and mark the size data and material data of each furniture part to obtain part data.

[0013] Optionally, the machining deformation prediction is performed based on the initial machining process information determined by the part data and the cutting temperature model using residual stress analysis to obtain machining deformation prediction information, and the initial machining process information is corrected based on the machining deformation prediction information to obtain target machining process information, including:

[0014] Determine the machining quality target based on the part data, and determine the preliminary machining process information based on the machining quality target;

[0015] Construct a cutting temperature model based on the preliminary machining process information using the least square method, and analyze the optimization objective function of the preliminary machining process information based on the cutting temperature model using the linear weighting method, adjust the preliminary machining process information based on the optimization objective function, and obtain the initial machining process information;

[0016] Perform residual stress analysis based on the initial machining process information using the finite element model of the machining part to obtain target residual stress;

[0017] Perform machining deformation prediction based on the target residual stress to obtain machining deformation prediction information;

[0018] Correct the initial machining process information based on the machining deformation prediction information to obtain the target machining process information.

[0019] Optionally, the target machining process information is used to perform machining equipment scheduling analysis to obtain target scheduling machining equipment, including:

[0020] Perform load state analysis on each machining equipment based on the target machining process information to obtain load state information;

[0021] A constraint programming model of the processing equipment is established based on the scheduling constraint data, and scheduling analysis of the processing equipment is performed based on the constraint programming model and the load state information, so as to obtain the target scheduling processing equipment.

[0022] Optionally, the device state detection information is obtained by performing device state detection on the target scheduling processing equipment based on the target processing process information and in combination with response surface modeling, and the device state detection information includes:

[0023] The running data collected by running the target scheduling processing equipment according to the target processing process information is preprocessed, so as to obtain preprocessed running data.

[0024] The preprocessed running data is subjected to feature extraction and feature dimension reduction, so as to obtain a device state feature vector.

[0025] The response surface model is obtained by performing response surface modeling based on a central composite experimental design, and fuzzy analysis is performed based on the response surface model, so as to obtain a device wear response curve.

[0026] The precision retention capability data of the target scheduling processing equipment is analyzed based on the performance degradation data of the target scheduling processing equipment by using a precision retention capability evaluation model and a Bayesian algorithm.

[0027] The device state detection information is obtained by performing device state detection based on the device state feature vector, the device wear response curve and the precision retention capability data.

[0028] Optionally, the target processing process information is adjusted based on the device state detection information, so as to obtain adjusted target processing process information, and the adjusting includes:

[0029] The adjustment step information is obtained by performing adjustment step analysis on the target processing process information based on the device state detection information.

[0030] The coupling effect analysis data is obtained by performing coupling effect analysis based on the adjustment step information by using an adaptive coupling regulator.

[0031] The adjusted target processing process information is obtained by adjusting the target processing process information based on the processing energy consumption model and the processing time model by using the coupling effect analysis data.

[0032] Optionally, furniture manufacturing simulation is performed based on the adjusted target processing process information, and the adjusted target processing process information is optimized based on the furniture manufacturing simulation result, so as to obtain optimized target processing process information, and the optimizing includes:

[0033] The furniture manufacturing simulation result is obtained by inputting the adjusted target processing process information into simulation software for furniture manufacturing simulation.

[0034] Feedback data is extracted based on the furniture manufacturing simulation results, and the adjusted target processing technology information is optimized based on the feedback data to obtain the optimized target processing technology information.

[0035] In addition, the present invention also provides a processing information analysis and optimization device for furniture manufacturing, the device comprising:

[0036] Data splitting module: used to split the furniture model data of the target furniture in the furniture manufacturing plan and obtain the parts data of the target furniture;

[0037] Processing information correction module: used to predict machining deformation using residual stress analysis based on initial machining process information determined by part data and cutting temperature model, obtain machining deformation prediction information, and correct the initial machining process information based on the machining deformation prediction information to obtain target machining process information;

[0038] Equipment scheduling analysis module: used to perform processing equipment scheduling analysis based on target processing technology information to obtain target scheduling processing equipment;

[0039] Equipment status detection module: used to detect the equipment status of the target scheduling processing equipment based on the target processing technology information combined with response surface modeling to obtain equipment status detection information;

[0040] Information adjustment module: used to adjust the target processing information based on the equipment status detection information to obtain the adjusted target processing information;

[0041] Information optimization module: used to perform furniture manufacturing simulation based on the adjusted target processing technology information, and optimize the adjusted target processing technology information based on the furniture manufacturing simulation results, obtain the optimized target processing technology information, and control the target scheduling processing equipment to manufacture furniture according to the optimized target processing technology information.

[0042] In addition, the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory so that the electronic device executes the above-mentioned processing information analysis and optimization method for furniture manufacturing.

[0043] In addition, the present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned processing information analysis and optimization method for furniture manufacturing.

[0044] In an embodiment of the present invention, the furniture model data of the target furniture in the furniture manufacturing plan is split into orders to obtain the part data of the target furniture, thereby improving the analysis efficiency of the processing information. Based on the initial processing information determined by the part data and the cutting temperature model, residual stress analysis is used to predict processing deformation. The initial processing information is corrected based on the processing deformation prediction information, thereby improving the accuracy of the processing deformation prediction, greatly reducing the impact of processing deformation, and making the obtained target processing information more reliable. Processing equipment scheduling analysis is performed based on the target processing information. Based on the target processing information combined with response surface modeling, equipment status detection is performed on the target scheduled processing equipment. Equipment status detection is performed on the processing equipment to be scheduled, so as to more clearly understand the wear and performance changes of the equipment. The target processing information is adjusted based on the equipment status detection information, so that the obtained adjusted target processing information is more accurate, which can effectively improve the processing accuracy of the furniture. Furniture manufacturing simulation is performed based on the adjusted target processing information to optimize the adjusted target processing information, further ensuring the reliability of the processing information and making the manufacturing and processing of furniture closer to the ideal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 1 is a flow chart of a processing information analysis and optimization method for furniture manufacturing in an embodiment of the present invention;

[0047] Figure 2 is a flow chart of a processing information analysis and optimization method for furniture manufacturing in another embodiment of the present invention;

[0048] Figure 3 1 is a schematic diagram of the structural composition of a processing information analysis and optimization device for furniture manufacturing in an embodiment of the present invention;

[0049] Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those ordinarily skilled in the art without creative effort belong to the scope of the present application.

[0051] Embodiment one

[0052] Please refer to Figure 1 , Figure 1 is a flowchart of a processing information analysis and optimization method for furniture manufacturing in the embodiments of the present application, and the method comprises:

[0053] S11: disassembling the furniture model data of the target furniture in the furniture manufacturing scheme to obtain the part data of the target furniture;

[0054] In the specific implementation process of the present application, the disassembling the furniture model data of the target furniture in the furniture manufacturing scheme to obtain the part data of the target furniture comprises: extracting the three-dimensional model of the target furniture, disassembling the three-dimensional model to obtain a plurality of furniture parts, and marking the size data and material data of each furniture part to obtain the part data.

[0055] Specifically, the furniture manufacturing scheme contains the three-dimensional model of each target furniture to be manufactured, the three-dimensional model of the target furniture is extracted, the three-dimensional model is disassembled, i.e. the three-dimensional model of the target furniture is disassembled into a plurality of parts, i.e. a plurality of furniture parts are obtained, and the size data and material data of each furniture part are marked to generate the part data from the size data and material data.

[0056] S12: using residual stress analysis to predict the processing deformation based on the initial processing information determined by the part data and the cutting temperature model to obtain processing deformation prediction information, and correcting the initial processing information based on the processing deformation prediction information to obtain the target processing information;

[0057] In the implementation of the present application, the machining deformation prediction is performed based on the initial machining process information determined by the part data and the cutting temperature model, the machining deformation prediction information is obtained, the initial machining process information is corrected based on the machining deformation prediction information, and the target machining process information is obtained, including: determining the machining quality target based on the part data, and determining the preliminary machining process information based on the machining quality target; constructing the cutting temperature model based on the preliminary machining process information by using the least square method, and analyzing the optimization objective function of the preliminary machining process information based on the cutting temperature model by using the linear weighting method, adjusting the preliminary machining process information based on the optimization objective function, and obtaining the initial machining process information; performing residual stress analysis based on the initial machining process information by using the finite element model of the machined part, and obtaining the target residual stress; performing machining deformation prediction based on the target residual stress, and obtaining the machining deformation prediction information; correcting the initial machining process information based on the machining deformation prediction information, and obtaining the target machining process information.

[0058] Specifically, the machining quality target is determined based on the part data, the machining quality target includes the quality-related targets of each machining equipment in the machining process, such as the depth of laser machining, roughness and the like of the laser cutting equipment, and the preliminary machining process information is determined based on the machining quality target, the corresponding preliminary machining process information is matched in the database according to the machining quality target, the machining process information includes the operation parameters of the machining equipment involved in the furniture machining process, such as the cutting process of the furniture part, the value range of the operation parameters of the cutting equipment, the tool structure parameters of the cutting equipment, the edge trimming process of the furniture part, the value range of the operation power of the edge trimming equipment and the like. The cutting temperature model is constructed by using the least square method based on the preliminary machining process information, the cutting machining process information is extracted from the preliminary machining process information, the cutting machining process information includes the value range of the operation parameters of the cutting equipment and the tool structure parameters, the operation parameters such as cutting force and cutting speed and the like, the orthogonal test is carried out according to the value range of the operation parameters and the tool structure parameters, the tool structure parameters are fixed, the cutting simulation test is carried out for each value of the operation parameters, the cutting temperature data of each cutting simulation test is recorded, the nonlinear least square method is used to fit the recorded cutting temperature data, the cutting temperature model is obtained, and the optimization objective function of the preliminary machining process information is analyzed based on the cutting temperature model by using the linear weighting method. In the manufacturing and machining of furniture, the balance between surface quality, tool life and machining cost needs to be achieved in cutting, therefore the surface quality, tool life and machining cost corresponding to each cutting temperature data in the cutting temperature model are obtained, according to each cutting temperature data in the cutting temperature model and the corresponding surface quality, tool life and machining cost, the optimization objective function is generated by using the weight, the preliminary machining process information is adjusted based on the optimization objective function, the adjustment value of the preliminary machining process information can be determined by using the optimization objective function through the circulation method, the corresponding objective function value is calculated according to each cutting temperature data, the corresponding surface quality, tool life and machining cost and the weight thereof, the operation parameters of the cutting equipment corresponding to the best objective function value are selected, the operation parameters are taken as the required cutting machining process information, the overall preliminary machining process information is adjusted according to the cutting machining process information, such as adjusting the speed and feed amount of the lathe according to the cutting speed of the cutting machining process information, the initial machining process information is obtained, the cutting temperature directly affects the tool wear and the workpiece surface quality, the thermal stress generated by discontinuous cutting in the cutting process can accelerate the fatigue failure and wear of the tool, therefore the initial machining process information is determined by the cutting temperature model, which can reduce the influence of the cutting temperature on the machining quality.Based on the initial processing process information, the residual stress analysis is performed by using the finite element model of the processed part. The processing simulation and mesh division of the furniture part are performed in the finite element simulation software according to the initial processing process information, and the finite element model of the processed part is obtained. The three-dimensional coordinate information of the finite element model of the processed part is extracted, and the displacement data and strain data of the finite element model of the processed part are calculated according to the extracted three-dimensional coordinate information. The corresponding residual stress nephogram is calculated by using the nonlinear finite element calculation software according to the displacement data and strain data. The nodes in the residual stress solving area on the residual stress nephogram are determined according to the step of the residual stress nephogram. The center node position is determined according to the distribution form of all nodes. The residual stress of each node on the residual stress nephogram is extracted with the center node position as the center, that is, the target residual stress is obtained. The residual stress refers to the action and influence of various process factors on the component during the manufacturing process. When these factors disappear, if the action and influence of the component cannot completely disappear, part of the action and influence remains in the component, and this residual action and influence is called residual stress. Based on the target residual stress, the processing deformation prediction is performed. The finite element simulation of crystal deformation in the processing process is performed according to the initial processing process information and the target residual stress, that is, the plane strain compression simulation and tensile simulation of the crystal in the processing process are performed through the initial processing process information and the target residual stress. Therefore, the crystal change in the processing process can be known. The deformation information in the processing process of the furniture part is predicted from the finite element simulation information of the crystal deformation and the target residual stress. The prediction of the deformation information can be assisted by the deep neural network, that is, the processing deformation prediction information is obtained. Based on the processing deformation prediction information, the initial processing process information is corrected. The correction value of the parameter in the initial processing process information can be determined in the database according to the processing deformation prediction information. The initial processing process information is corrected by the correction value, and the target processing process information is obtained. Therefore, the target processing process information obtained can better consider the influence of the processing deformation.

[0059] S13: Based on the target processing process information, the processing equipment scheduling analysis is performed to obtain the target scheduling processing equipment.

[0060] In the specific implementation process of the present application, based on the target processing process information, the processing equipment scheduling analysis is performed to obtain the target scheduling processing equipment, which includes: based on the target processing process information, the load state analysis of each processing equipment is performed to obtain the load state information; the constraint planning model of the processing equipment is established based on the scheduling constraint data, and the processing equipment scheduling analysis is performed based on the constraint planning model and the load state information to obtain the target scheduling processing equipment.

[0061] Specifically, based on the target processing process information, the load state of each processing equipment is analyzed, the running time and the executed processing task amount of each processing equipment are obtained, the working burden of each processing equipment in the case of executing the processing task according to the target processing process information is analyzed according to the running time and the executed processing task amount, and the load state information is obtained. The constraint programming model of the processing equipment is established based on the scheduling constraint data, the scheduling constraint data includes the limit capacity, the bearable load and the like of each processing equipment, the constraint programming model of the processing equipment is established according to the scheduling constraint data, that is, the running limit value of the processing equipment is established according to the scheduling constraint data, the model for constraining the running of the processing equipment is established through the running limit value, and the processing equipment scheduling analysis is performed based on the constraint programming model and the load state information, that is, the working equipment whose load state information exceeds the constraint programming model is screened out, the processing equipment required according to the target processing process information is screened from the remaining processing equipment, the processing equipment with smaller load can be selected from the remaining processing equipment, and the target scheduling processing equipment is obtained.

[0062] S14: performing equipment state detection on the target scheduling processing equipment based on the target processing process information and response surface modeling, and obtaining equipment state detection information;

[0063] In the specific implementation process of the present application, the equipment state detection on the target scheduling processing equipment based on the target processing process information and response surface modeling, and the obtaining of the equipment state detection information, includes: preprocessing the running data collected during the running of the target scheduling processing equipment according to the target processing process information, to obtain preprocessed running data; performing feature extraction and feature dimension reduction on the preprocessed running data, to obtain an equipment state feature vector; performing response surface modeling based on central composite experimental design, to obtain a response surface model, and performing fuzzy analysis based on the response surface model, to obtain an equipment wear response curve; analyzing the precision retention capability data of the target scheduling processing equipment based on the performance degradation data of the target scheduling processing equipment by using a precision retention capability evaluation model and a Bayesian algorithm; and performing equipment state detection based on the equipment state feature vector, the equipment wear response curve and the precision retention capability data, to obtain the equipment state detection information.

[0064] Specifically, the running data collected by the target scheduling machining equipment running according to the target machining process information is preprocessed, that is, when the target scheduling machining equipment is simulated to run according to the target machining process information, the running data of each time point is collected, such as the cutting force, tool vibration, cutting speed and feed rate of the cutting equipment running according to the target machining process information, the preprocessing of the running data includes data cleaning and data format standardization, and the preprocessed running data is obtained. The preprocessed running data is feature extracted and feature reduced, the mean, variance, peak value, skewness and kurtosis of the preprocessed running data of each time point are calculated, the mean represents the average value of the data, the variance represents the dispersion degree of the data, the peak value is the maximum value of the data, the skewness reflects the symmetry of the data distribution, and the kurtosis describes the sharpness of the data distribution, and the time domain feature set is composed of the mean, variance, peak value, skewness and kurtosis. The preprocessed running data is wavelet packet transformed to obtain a time-frequency coefficient matrix, the wavelet packet transform can provide time and frequency information at the same time, the energy entropy and relative energy are calculated by analyzing the time-frequency coefficient matrix to form the time-frequency feature set, the energy entropy describes the complexity of the signal energy distribution, and the relative energy represents the energy distribution of different frequency bands. The time domain feature set and the time-frequency feature set are combined to obtain a combined feature set, the combined feature set is segmented, the phased feature set is obtained according to different time stages in the machining process, the phased feature set can reflect the feature change of different stages, and the equipment state feature vector is determined by the phased feature set.Based on the central composite experimental design, the center composite experimental design is used for the response surface method, which can effectively model the response surface. In the processing technology information, select the processing technology information that needs to be studied as the test factor, take three levels of low, medium and high for the test factor, combine these levels to get the test factor level matrix. According to the test factor level matrix, the device state feature vector is grouped. For example, in one test, set the cutting speed and cutting depth as test factors, take a certain value as the test condition, collect the device state feature vector of the processing equipment under this test condition, and then record the test condition and test factor level corresponding to the device state feature vector. Form the characteristic response data set of the device state feature vector, test condition and level of each test factor. The least square method is used to fit the characteristic response data set to obtain the response surface model. Based on the response surface model, the contour plot and three-dimensional surface plot of the response surface model are drawn to generate the visual response surface plot. These graphs can intuitively show the influence of different processing parameters on the response variable. For example, through the contour plot, the change trend of the cutting tool wear under different combinations of cutting speed and feed rate is reflected. Through the three-dimensional surface plot, the change of the cutting tool wear in the three-dimensional space of cutting speed, feed rate and cutting depth is observed. The visual response surface plot is fuzzified to obtain the fuzzy response surface plot. Fuzzification is used to handle the uncertainty and fuzziness in the data. According to the fuzzy response surface plot, interpolation and smoothing processing are carried out, that is, the response value of the fuzzy response surface plot is interpolated and smoothed to form a continuous response curve, that is, the device wear response curve is obtained. This curve can accurately describe the change of the device wear under different combinations of processing parameters. Based on the performance degradation data of the target scheduling processing equipment, the precision retention capability evaluation model and the Bayesian algorithm are used to analyze the precision retention capability data of the target scheduling processing equipment. The performance degradation data is searched in the parameter library. The precision retention capability evaluation model uses a neural network model. In the input layer of the precision retention capability evaluation model, naive Bayes is used for feature selection and extraction. The obtained feature vector is input into the model for prediction, so as to obtain the precision retention capability data of the processing equipment. Based on the device state feature vector, the device wear response curve and the precision retention capability data, the device state detection is carried out. According to the device state feature vector and the device wear response curve, the wear change of the device under the target processing technology information is determined. According to the wear change of the device and the precision retention capability data, the state change of the target scheduling processing equipment is analyzed, that is, the device state detection information is obtained.

[0065] S15: Adjust the target processing technology information based on the device state detection information to obtain the adjusted target processing technology information.

[0066] In the implementation of the present application, the adjustment of the target machining process information based on the equipment state detection information comprises: adjusting step analysis of the target machining process information based on the equipment state detection information to obtain adjustment step information; coupling effect analysis based on the adjustment step information using an adaptive coupling regulator to obtain coupling effect analysis data; and adjustment of the target machining process information based on the machining energy consumption model and the machining time model using the coupling effect analysis data to obtain the adjusted target machining process information.

[0067] Specifically, the target machining process information is analyzed based on the equipment state detection information to obtain adjustment step information, that is, the direction in which each parameter in the target machining process information should be increased or decreased and the adjustment amplitude are analyzed based on the equipment state detection information. Coupling effect analysis is performed based on the adjustment step information using an adaptive coupling regulator. The adaptive coupling regulator selects one of the parameter types in the machining process information as a research parameter type, and takes the research parameter type as an independent variable, such as selecting cutting speed as the independent variable, and takes the remaining parameter types as constants. For each change in the independent variable, the changes in the control targets corresponding to the other parameter types are observed and analyzed, and the changes in the machining quality are observed and analyzed. Different parameter types are selected each time to repeat the above processing, the mutual influence and coupling effect between the parameters are analyzed, and the coupling parameter groups with coupling effect indexes greater than a preset coupling effect index are identified, that is, the parameter changes in these groups have a significant impact on the control target. At the same time, each coupling parameter group is labeled with its coupling effect relationship. Based on the coupling parameter groups and the coupling effect relationship labels, the adjustment logic of the adaptive coupling regulator is designed. This logic should be able to automatically identify and respond to coupling effects according to the current machining conditions and parameter states. The adjustment step information is input into the adaptive coupling regulator, which analyzes the coupling effect between the parameters in the machining process information based on the adjustment step information and its internal coupling effect logic, that is, analyzes the influence of the interaction between the parameters on the machining process under the current adjustment step. The adaptive coupling regulator generates coupling effect, that is, obtains coupling effect analysis data, which quantifies the coupling effect strength and direction between the parameters under the current adjustment step. The target machining process information is adjusted based on the machining energy consumption model and the machining time model using the coupling effect analysis data. It is determined whether the coupling effect analysis data is consistent with the expected target. If the coupling effect analysis data is not consistent with the expected target, such as causing the machining efficiency to decrease, reverse parameter adjustment is performed, that is, the adjustment step information is adjusted based on the coupling effect analysis data to reduce or eliminate adverse coupling effects, until the deviation between the obtained coupling effect analysis data and the expected target satisfies a preset deviation threshold. The adjustment step information is taken as the final adjustment step. If the coupling effect analysis data is consistent with the expected target, the adjustment step information is directly taken as the final adjustment step. The energy consumption of furniture manufacturing is analyzed by the machining energy consumption model after the target machining process information is adjusted by the adjustment step. The time of furniture manufacturing is analyzed by the machining time model after the target machining process information is adjusted by the adjustment step. The deviations of the energy consumption and the time from the preset thresholds are calculated, the adjustment step is adjusted according to the deviations, the target machining process information is adjusted by the adjusted adjustment step, and the adjusted target machining process information is obtained. The obtained adjusted target machining process information takes into account the state changes of the machining equipment while considering the machining energy consumption and the machining time.

[0068] S16: Simulate furniture manufacturing based on the adjusted target machining process information, and optimize the adjusted target machining process information based on a simulation result of the furniture manufacturing simulation to obtain optimized target machining process information, and control the target scheduling machining equipment to perform furniture manufacturing according to the optimized target machining process information.

[0069] In the implementation of the present application, the simulation of furniture manufacturing based on the adjusted target machining process information, and the optimization of the adjusted target machining process information based on a simulation result of the furniture manufacturing simulation to obtain optimized target machining process information, comprises: inputting the adjusted target machining process information into simulation software to simulate furniture manufacturing, and obtaining a simulation result of the furniture manufacturing simulation; extracting feedback data based on the simulation result of the furniture manufacturing simulation, and optimizing the adjusted target machining process information based on the feedback data to obtain the optimized target machining process information.

[0070] Specifically, the adjusted target machining process information is input into simulation software to simulate furniture manufacturing, and a simulation result of the furniture manufacturing simulation is obtained; feedback data is extracted based on the simulation result of the furniture manufacturing simulation, that is, the simulation result of the furniture manufacturing simulation is compared with an expected effect, and a deviation from the expected effect is analyzed to obtain the feedback data, and the adjusted target machining process information is optimized based on the feedback data, a corresponding optimization value is matched according to the feedback data, the adjusted target machining process information is optimized according to the corresponding optimization value, and the optimized target machining process information is obtained, thereby further ensuring the reliability of the machining process information, and the target scheduling machining equipment is controlled to perform furniture manufacturing according to the optimized target machining process information, so that the furniture manufacturing is more reliable.

[0071] Embodiment Two

[0072] Please refer to Figure 2 , Figure 2 is a flowchart of a machining information analysis and optimization method for furniture manufacturing in another embodiment of the present application, and the method comprises:

[0073] S201: disassembling furniture model data of a target furniture in a furniture manufacturing scheme to obtain part data of the target furniture;

[0074] S202: performing machining deformation prediction using residual stress analysis based on initial machining process information determined by the part data and a cutting temperature model to obtain machining deformation prediction information, and correcting the initial machining process information based on the machining deformation prediction information to obtain target machining process information;

[0075] S203: performing machining equipment scheduling analysis based on the target machining process information to obtain target scheduling machining equipment;

[0076] S204: preprocessing the running data collected by the target scheduling machining equipment according to the target machining process information, to obtain preprocessed running data;

[0077] S205: feature extraction and feature dimension reduction are performed on the preprocessed running data, to obtain an equipment state feature vector;

[0078] S206: response surface modeling is performed based on central composite experimental design, to obtain a response surface model, and fuzzy analysis is performed based on the response surface model, to obtain an equipment wear response curve;

[0079] S207: the precision retention capability data of the target scheduling machining equipment is analyzed based on the performance degradation data of the target scheduling machining equipment using the precision retention capability evaluation model and the Bayesian algorithm;

[0080] S208: equipment state detection is performed based on the equipment state feature vector, the equipment wear response curve and the precision retention capability data, to obtain equipment state detection information;

[0081] S209: the target machining process information is adjusted based on the equipment state detection information, to obtain adjusted target machining process information;

[0082] S210: furniture manufacturing simulation is performed based on the adjusted target machining process information, and the adjusted target machining process information is optimized based on the furniture manufacturing simulation result, to obtain optimized target machining process information, and the target scheduling machining equipment is controlled to manufacture furniture according to the optimized target machining process information.

[0083] Embodiment three

[0084] Please refer to Figure 3 , Figure 3 is a structural composition schematic diagram of a machining information analysis and optimization device for furniture manufacturing in the embodiment of the application, which comprises:

[0085] The data unpacking module 31 is configured to unpack the furniture model data of the target furniture in the furniture manufacturing scheme, to obtain part data of the target furniture.

[0086] The machining information correction module 32 is configured to perform machining deformation prediction based on the initial machining process information determined by the part data and the cutting temperature model using residual stress analysis, to obtain machining deformation prediction information, and correct the initial machining process information based on the machining deformation prediction information, to obtain target machining process information.

[0087] The equipment scheduling analysis module 33 is configured to perform machining equipment scheduling analysis based on the target machining process information, to obtain target scheduling machining equipment.

[0088] The device state detection module 34 is configured to detect the device state of the target scheduling processing device based on the target processing information and a response surface modeling, and obtain device state detection information.

[0089] The information adjustment module 35 is configured to adjust the target processing information based on the device state detection information, and obtain adjusted target processing information.

[0090] The information optimization module 36 is configured to simulate the furniture manufacturing based on the adjusted target processing information, and optimize the adjusted target processing information based on the simulation result, and obtain optimized target processing information, so that the target scheduling processing device manufactures the furniture according to the optimized target processing information.

[0091] In the implementation of the present application, the implementation of the device item can refer to the implementation of the method item as described above, which will not be repeated here.

[0092] The computer readable storage medium provided by the embodiment of the present application stores a computer program, and the program is executed by a processor to realize the processing information analysis and optimization method for furniture manufacturing in any one of the above embodiments. The computer readable storage medium includes but is not limited to any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. That is, the storage device includes any medium that stores or transmits information in a readable form by a device (for example, a computer, a mobile phone), which can be a read-only memory, a magnetic disk or an optical disk, etc.

[0093] Embodiment four

[0094] Please refer to Figure 4 , Figure 4 is a structural composition schematic diagram of the electronic device in the embodiment of the present application.

[0095] The embodiment of the present application further provides an electronic device, as shown in the figure, Figure 4 The electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. Those skilled in the art can understand that Figure 3The electronic device shown does not constitute a limitation on all devices, and can include more or fewer components than shown, or combine certain components. The memory 41 can be used to store computer programs 42 and various functional modules, and the processor 43 runs the computer programs 42 stored in the memory 41 to perform various functional applications and data processing of the device. The memory can be an internal memory or an external memory, or include both an internal memory and an external memory. The internal memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. The external memory can include a hard disk, a floppy disk, a ZIP disk, a U disk, a magnetic tape, etc. The processor 43 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, a single-chip processor, or the processor 43 can be any conventional processor, etc. The processor and the memory disclosed in the present application include but are not limited to these types of processors and memories. The processor and the memory disclosed in the present application are only examples and not limitations.

[0096] As an embodiment, the electronic device includes one or more processors 43, a memory 41, and one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to perform the processing information analysis optimization method for furniture manufacturing in any one of the above embodiments. For details, please refer to the above embodiments.

[0097] In addition, the above describes in detail a processing information analysis optimization method for furniture manufacturing and related devices provided by the embodiments of the present application. The principles and implementation manners of the present application are described by using specific examples in this paper. The above description of the embodiments is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A processing information analysis and optimization method for furniture manufacturing, characterized in that: The method comprises: Decompose the furniture model data of the target furniture in the furniture manufacturing plan to obtain the parts data of the target furniture; Based on the initial processing information determined by the part data and the cutting temperature model, residual stress analysis is used to predict processing deformation to obtain processing deformation prediction information, and the initial processing information is corrected based on the processing deformation prediction information to obtain target processing information, including: determining a processing quality target based on the part data, and determining preliminary processing information based on the processing quality target; constructing a cutting temperature model based on the preliminary processing information using the least squares method, and analyzing the optimization objective function of the preliminary processing information based on the cutting temperature model using the linear weighted method, adjusting the preliminary processing information based on the optimization objective function to obtain initial processing information; based on the initial processing information, residual stress analysis is performed using a finite element model of the processed part to obtain target residual stress; based on the target residual stress, processing deformation is predicted to obtain processing deformation prediction information; based on the processing deformation prediction information, the initial processing information is corrected to obtain target processing information; Perform processing equipment scheduling analysis based on target processing technology information to obtain target scheduling processing equipment; Based on the target processing technology information combined with response surface modeling, the target scheduling processing equipment is subjected to equipment status detection to obtain equipment status detection information, including: preprocessing the operation data collected when the target scheduling processing equipment is operated according to the target processing technology information to obtain preprocessed operation data; performing feature extraction and feature dimension reduction on the preprocessed operation data to obtain an equipment status feature vector; performing response surface modeling based on a central composite experimental design to obtain a response surface model, and performing fuzzy analysis based on the response surface model to obtain an equipment wear response curve; analyzing the accuracy retention capability data of the target scheduling processing equipment using an accuracy retention capability evaluation model and a Bayesian algorithm based on the performance degradation data of the target scheduling processing equipment; performing equipment status detection based on the equipment status feature vector, the equipment wear response curve, and the accuracy retention capability data to obtain equipment status detection information; Adjusting target processing information based on equipment status detection information to obtain adjusted target processing information; Furniture manufacturing simulation is performed based on the adjusted target processing technology information, and the adjusted target processing technology information is optimized based on the furniture manufacturing simulation results to obtain the optimized target processing technology information, and the target scheduling processing equipment is controlled to perform furniture manufacturing according to the optimized target processing technology information.

2. The processing information analysis and optimization method for furniture manufacturing according to claim 1 is characterized in that: The step of splitting the furniture model data of the target furniture in the furniture manufacturing plan to obtain the parts data of the target furniture includes: A three-dimensional model of the target furniture is extracted, the three-dimensional model is broken down into individual pieces to obtain a number of furniture parts, and the size data and material data of each furniture part are marked to obtain part data.

3. The processing information analysis and optimization method for furniture manufacturing according to claim 1, characterized in that: The processing equipment scheduling analysis based on the target processing technology information to obtain the target scheduling processing equipment includes: Perform load status analysis on each processing equipment based on target processing technology information to obtain load status information; A constraint programming model for processing equipment is established based on the scheduling constraint data, and processing equipment scheduling analysis is performed based on the constraint programming model and load status information to obtain the target scheduling processing equipment.

4. The processing information analysis and optimization method for furniture manufacturing according to claim 1, characterized in that: The step of adjusting the target processing information based on the equipment status detection information to obtain the adjusted target processing information includes: Performing adjustment step analysis on target processing information based on equipment status detection information to obtain adjustment step information; Based on the adjustment step information, an adaptive coupling regulator is used to perform coupling effect analysis to obtain coupling effect analysis data; The target processing information is adjusted based on the processing energy consumption model and the processing time model using coupling effect analysis data to obtain the adjusted target processing information.

5. The processing information analysis and optimization method for furniture manufacturing according to claim 1, characterized in that: The performing of furniture manufacturing simulation based on the adjusted target processing technology information, and optimizing the adjusted target processing technology information based on the furniture manufacturing simulation result to obtain the optimized target processing technology information, includes: Inputting the adjusted target processing information into the simulation software to perform furniture manufacturing simulation and obtain the furniture manufacturing simulation results; Feedback data is extracted based on the furniture manufacturing simulation results, and the adjusted target processing technology information is optimized based on the feedback data to obtain the optimized target processing technology information.

6. A processing information analysis and optimization device for furniture manufacturing, characterized in that: The device comprises: Data splitting module: used to split the furniture model data of the target furniture in the furniture manufacturing plan and obtain the parts data of the target furniture; Processing information correction module: used to predict processing deformation using residual stress analysis based on initial processing information determined by part data and cutting temperature model to obtain processing deformation prediction information, and correct the initial processing information based on the processing deformation prediction information to obtain target processing information, including: determining a processing quality target based on part data, and determining preliminary processing information based on the processing quality target; constructing a cutting temperature model based on the preliminary processing information using the least squares method, and analyzing the optimization objective function of the preliminary processing information based on the cutting temperature model using the linear weighted method, adjusting the preliminary processing information based on the optimization objective function to obtain initial processing information; performing residual stress analysis using a finite element model of the processed part based on the initial processing information to obtain target residual stress; predicting processing deformation based on the target residual stress to obtain processing deformation prediction information; and correcting the initial processing information based on the processing deformation prediction information to obtain target processing information. Equipment scheduling analysis module: used to perform processing equipment scheduling analysis based on target processing technology information to obtain target scheduling processing equipment; Equipment status detection module: used to perform equipment status detection on target scheduling processing equipment based on target processing technology information combined with response surface modeling to obtain equipment status detection information, including: preprocessing the operation data collected when the target scheduling processing equipment operates according to the target processing technology information to obtain preprocessed operation data; performing feature extraction and feature dimension reduction on the preprocessed operation data to obtain equipment status feature vectors; performing response surface modeling based on central composite experimental design to obtain a response surface model, and performing fuzzy analysis based on the response surface model to obtain an equipment wear response curve; analyzing the accuracy retention capability data of the target scheduling processing equipment using the accuracy retention capability evaluation model and the Bayesian algorithm based on the performance degradation data of the target scheduling processing equipment; performing equipment status detection based on the equipment status feature vector, the equipment wear response curve and the accuracy retention capability data to obtain equipment status detection information; Information adjustment module: used to adjust the target processing information based on the equipment status detection information to obtain the adjusted target processing information; Information optimization module: used to perform furniture manufacturing simulation based on the adjusted target processing technology information, and optimize the adjusted target processing technology information based on the furniture manufacturing simulation results, obtain the optimized target processing technology information, and control the target scheduling processing equipment to manufacture furniture according to the optimized target processing technology information.

7. An electronic device comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the processing information analysis and optimization method for furniture manufacturing as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the processing information analysis and optimization method for furniture manufacturing according to any one of claims 1 to 5.

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