Processing information analysis optimization method for furniture manufacturing and related device

Abstract

The invention discloses a processing information analysis optimization method for furniture manufacturing and a related device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the order splitting of furniture model data, and obtaining the part data of target furniture; performing machining deformation prediction based on initial machining process information determined by the part data and the cutting temperature model to correct the initial machining process information; processing equipment scheduling analysis is carried out based on the target processing technology information; performing equipment state detection on the target scheduling processing equipment based on the target processing technology information in combination with response surface modeling; adjusting the target processing technology information based on the equipment state detection information; furniture manufacturing analogue simulation is carried out based on the adjusted target processing technology information to optimize the processing technology information, and the control equipment carries out furniture manufacturing according to the optimized target processing technology information. According to the method, the reliability of processing technology information analysis can be guaranteed, and the manufacturing and processing of furniture are closer to an ideal effect.

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: 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 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; 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 equipment status detection of the target scheduling processing equipment is carried out to obtain the 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.

[0005] Optionally, 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.

[0006] Optionally, the performing of machining deformation prediction using residual stress analysis based on initial machining process information determined by the part data and the 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 includes: Determining a machining quality target based on the part data, and determining preliminary machining process information based on the machining quality target; Based on the preliminary processing information, the least square method is used to construct a cutting temperature model, and the linear weighted method is used to analyze the optimization objective function of the preliminary processing information based on the cutting temperature model. Based on the optimization objective function, the preliminary processing information is adjusted to obtain the initial processing information. Based on the initial processing information, the finite element model of the machined part is used to perform residual stress analysis to obtain the target residual stress; Predict machining deformation based on target residual stress to obtain machining deformation prediction information; The initial machining process information is modified based on the machining deformation prediction information to obtain the target machining process information.

[0007] Optionally, performing processing equipment scheduling analysis based on target processing technology information to obtain 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.

[0008] Optionally, performing equipment status detection on the target scheduling processing equipment based on the target processing technology information in combination with response surface modeling to obtain equipment status detection information includes: Preprocessing the operation data collected when the target scheduling processing equipment is operated according to the target processing process information to obtain preprocessed operation data; Perform feature extraction and dimension reduction on the pre-processed operating data to obtain the equipment status feature vector; Response surface modeling was performed based on central composite experimental design to obtain a response surface model, and fuzzy analysis was performed based on the response surface model to obtain the equipment wear response curve; Based on the performance degradation data of the target scheduling processing equipment, the accuracy retention capability evaluation model and Bayesian algorithm are used to analyze the accuracy retention capability data of the target scheduling processing equipment; The equipment status is detected based on the equipment status feature vector, the equipment wear response curve and the accuracy retention capability data to obtain the equipment status detection information.

[0009] Optionally, 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.

[0010] Optionally, performing 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 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.

[0011] In addition, the present invention also provides a processing information analysis and optimization device for furniture manufacturing, the device comprising: 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 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; 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 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; 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.

[0012] 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.

[0013] 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.

[0014] 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, the target scheduled processing equipment is tested for equipment status. The equipment status of the processing equipment to be scheduled is tested, and the wear and performance changes of the equipment are more clearly understood. The target processing information is adjusted based on the equipment status detection information, making the obtained adjusted target processing information 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

[0015] 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.

[0016] 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; Figure 2 is a flow chart of a processing information analysis and optimization method for furniture manufacturing in another embodiment of the present invention; 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; Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1 See also Figure 1 , Figure 1 : is a flow chart of a processing information analysis and optimization method for furniture manufacturing in an embodiment of the present invention, the method comprising: S11: Decomposing the furniture model data of the target furniture in the furniture manufacturing plan to obtain the parts data of the target furniture; In the specific implementation process of the present invention, the furniture model data of the target furniture in the furniture manufacturing plan is split into orders to obtain the parts data of the target furniture, including: extracting the three-dimensional model of the target furniture, splitting the three-dimensional model into orders to obtain several furniture parts, and marking the size data and material data of each furniture part to obtain the parts data.

[0019] Specifically, the furniture manufacturing plan includes a three-dimensional model of each target furniture to be manufactured. The three-dimensional model of the target furniture is extracted and the three-dimensional model is disassembled into several parts, that is, several furniture parts are obtained, and the size data and material data of each furniture part are marked, and the part data is generated from the size data and material data.

[0020] S12: performing machining deformation prediction using residual stress analysis based on initial machining process information determined by the part data and the cutting temperature model to obtain machining deformation prediction information, and modifying the initial machining process information based on the machining deformation prediction information to obtain target machining process information; In the specific implementation process of the present invention, the residual stress analysis is used to predict the machining deformation based on the initial machining process information determined by the part data and the cutting temperature model 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, 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 a cutting temperature model based on the preliminary machining process information using the least squares method, and analyzing the optimization objective function of the preliminary machining process information based on the cutting temperature model using the linear weighted method, adjusting the preliminary machining process information based on the optimization objective function to obtain the initial machining process information; performing residual stress analysis based on the initial machining process information using the finite element model of the machined part to obtain the target residual stress; predicting the machining deformation based on the target residual stress to obtain the machining deformation prediction information; and correcting the initial machining process information based on the machining deformation prediction information to obtain the target machining process information.

[0021] Specifically, the processing quality target is determined based on the part data. The processing quality target includes the quality-related targets of each processing equipment during the processing, such as the depth and roughness of laser processing of laser cutting equipment, and the preliminary processing process information is determined based on the processing quality target. The corresponding preliminary processing process information is matched in the database according to the processing quality target. The processing process information includes the operating parameters of the processing equipment involved in the furniture processing process, such as the cutting process of furniture parts, the value range of the operating parameters of the cutting equipment, the tool structure parameters of the cutting equipment, etc., the trimming process of furniture parts, and the value range of the operating power of the trimming equipment. Based on the preliminary processing information, the least squares method is used to construct a cutting temperature model, and the cutting process information is extracted from the preliminary processing information. The cutting process information includes the value range of the operating parameters of the cutting equipment and the tool structure parameters, and the operating parameters such as cutting force and cutting speed. Orthogonal experiments are carried out according to the value range of the operating parameters and the tool structure parameters. The tool structure parameters are fixed, and a cutting simulation test is carried out for each value of the operating parameters. The cutting temperature data of each cutting simulation experiment is recorded, and the nonlinear least squares method is used to fit the recorded cutting temperature data to obtain a cutting temperature model. Based on the cutting temperature model, the linear weighted method is used to analyze the optimization objective function of the preliminary processing information. In the manufacturing of furniture, its cutting needs to achieve a balance between surface quality, tool life and processing cost. Therefore, the surface quality, tool life and processing cost corresponding to each cutting temperature data in the cutting temperature model are obtained. and its corresponding surface quality, tool life and processing cost, and use their weights to generate an optimization objective function. Based on the optimization objective function, the preliminary processing technology information is adjusted. The adjustment value of the preliminary processing technology information can be determined by the optimization objective function through a loop method. According to each cutting temperature data, the corresponding surface quality, tool life and processing cost and their weights, the corresponding objective function value is calculated, and the operating parameters of the cutting equipment corresponding to the best objective function value are selected. The operating parameters are used as the required cutting processing technology information. According to the cutting processing technology information, the overall preliminary processing technology information is adjusted, such as adjusting the speed and feed rate of the lathe according to the cutting speed of the cutting processing technology information, etc., to obtain the initial processing technology information. The cutting temperature directly affects the tool wear and the surface quality of the workpiece. The thermal stress generated by discontinuous cutting during the cutting process will accelerate the fatigue damage and wear of the tool. Therefore, determining the initial processing technology information through the cutting temperature model can reduce the impact of the cutting temperature on the processing quality.Residual stress analysis is performed using a finite element model of the machined part based on initial machining process information. Machining simulation and meshing of the furniture part are performed in finite element simulation software based on the initial machining process information to obtain a finite element model of the machined part. Three-dimensional coordinate information is extracted from the finite element model of the machined part. Displacement data and strain data of the finite element model of the machined part are calculated based on the extracted three-dimensional coordinate information. A corresponding residual stress cloud map is calculated based on the displacement data and strain data using nonlinear finite element calculation software. Nodes within the residual stress solution area on the residual stress cloud map are determined based on the step size of the residual stress cloud map. The center node position is determined based on the distribution pattern of all nodes. The residual stress of each node is extracted on the residual stress cloud map with the center node position as the center, thereby obtaining the target residual stress. Residual stress refers to the effects and influences of various process factors on a component during the manufacturing process. When these factors disappear, if the above-mentioned effects and influences on the component cannot completely disappear, and some effects and influences still remain in the component, then this residual effect and influence is called residual stress. Machining deformation is predicted based on the target residual stress. Finite element simulation of crystal deformation during machining is performed based on the initial machining process information and the target residual stress. That is, plane strain compression simulation and tensile simulation of the crystal during machining are performed using the initial machining process information and the target residual stress. This allows the crystal changes during machining to be known. The deformation information of furniture parts during machining is predicted using the finite element simulation information of crystal deformation and the target residual stress. The prediction of deformation information can be assisted by a deep neural network, thereby obtaining machining deformation prediction information. The initial machining process information is corrected based on the machining deformation prediction information. The correction values of the parameters in the initial machining process information can be determined in the database based on the machining deformation prediction information. The initial machining process information is corrected using the correction values to obtain the target machining process information. The target machining process information thus obtained can better take into account the impact of machining deformation.

[0022] S13: Performing processing equipment scheduling analysis based on target processing technology information to obtain target scheduling processing equipment; In the specific implementation process of the present invention, the processing equipment scheduling analysis based on the target processing technology information to obtain the target scheduled processing equipment includes: performing load status analysis on each processing equipment based on the target processing technology information to obtain load status information; establishing a constraint planning model for the processing equipment based on the scheduling constraint data, and performing processing equipment scheduling analysis based on the constraint planning model and the load status information to obtain the target scheduled processing equipment.

[0023] Specifically, based on the target processing technology information, a load status analysis is performed on each processing equipment to obtain the operating time and the amount of processing tasks executed by each processing equipment. Based on the operating time and the amount of processing tasks executed, the workload of each processing equipment when executing processing tasks according to the target processing technology information is analyzed to obtain the load status information. A constraint planning model for the processing equipment is established based on the scheduling constraint data. The scheduling constraint data includes the maximum production capacity and the load that can be tolerated by each processing equipment. The constraint planning model for the processing equipment is established based on the scheduling constraint data, that is, the operating limit value of the processing equipment is established based on the scheduling constraint data. A model for constraining the operation of the processing equipment is established based on the operating limit value. The processing equipment scheduling analysis is performed based on the constraint planning model and the load status information. That is, working equipment whose load status information exceeds the constraint planning model is screened out. Among the remaining processing equipment, the processing equipment is screened according to the equipment required by the target processing technology information. The equipment with a smaller load can be selected from the remaining processing equipment to obtain the target scheduling processing equipment.

[0024] S14: performing equipment status detection on the target scheduling processing equipment based on the target processing technology information combined with response surface modeling to obtain equipment status detection information; In the specific implementation process of the present invention, the target scheduling processing equipment is detected based on the 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 is operated according to the target processing technology information to obtain preprocessed operation data; performing feature extraction and feature dimensionality reduction on the preprocessed operation data to obtain an equipment status 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 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.

[0025] Specifically, the operation data collected when the target scheduling processing equipment operates according to the target processing technology information is preprocessed, that is, when the target scheduling processing equipment is simulated and operated according to the target processing technology information, its operation data at each time point is collected. For example, when the cutting equipment operates according to the target processing technology information, its cutting force, tool vibration, cutting speed and feed rate are collected. The preprocessing of the operation data includes data cleaning and data format standardization to obtain the preprocessed operation data. The preprocessed operating data is subjected to feature extraction and feature dimensionality reduction. The mean, variance, peak, skewness and kurtosis of the operating data at each time point after preprocessing are analyzed. The mean represents the average value of the data, the variance represents the degree of dispersion of the data, the peak 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. The time domain feature set is composed of the mean, variance, peak, skewness and kurtosis. The preprocessed operating data is subjected to wavelet packet transform to obtain the time-frequency coefficient matrix. The wavelet packet transform can provide time and frequency information at the same time. By analyzing the time-frequency coefficient matrix, the energy entropy and relative energy are calculated to form a time-frequency feature set. The energy entropy describes the distribution complexity of the signal energy, while the relative energy represents the energy distribution in 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 to obtain a stage feature set according to the different time stages in the processing process. The stage feature set can reflect the feature changes in different stages, and the equipment state feature vector is determined by the stage feature set.Response surface modeling is performed based on central composite experimental design. Central composite experimental design is performed on each processing technology information. Central composite experimental design is a design scheme used for response surface method, which can effectively model response surface. Processing technology information that needs to be studied is selected as experimental factors in the processing technology information. Three levels are taken for the experimental factors, low level, middle level and high level. These levels are combined to obtain the experimental factor level matrix. The equipment state characteristic vectors are grouped according to the experimental factor level matrix. For example, in an experiment, cutting speed and cutting depth are set as experimental factors, and a certain value of them is used as the experimental condition. The equipment state characteristic vector of the processing equipment under the experimental condition is collected, and then the experimental conditions and experimental factor levels corresponding to the equipment state characteristic vector are recorded. The equipment state characteristic vector, experimental conditions and levels of each experimental factor form a characteristic response data set, and the characteristic response data set is minimized. The response surface model is obtained by square multiplication fitting, and fuzzy analysis is performed based on the response surface model. Contour maps and three-dimensional surface maps are drawn for the response surface model to generate visual response surface maps. These maps can intuitively show the influence of different processing parameters on the response variables. For example, the contour map can reflect the changing trend of cutting equipment wear under different cutting speed and feed rate combinations. The three-dimensional surface map can observe the changes in cutting equipment wear in the three-dimensional space of cutting speed, feed rate and cutting depth. The visual response surface map is fuzzified to obtain a fuzzy response surface map. Fuzzification is used to deal with uncertainty and ambiguity in the data. Interpolation and smoothing are performed according to the fuzzy response surface map, that is, the response values of the fuzzy response surface map are interpolated and smoothed to form a continuous response curve, that is, the equipment wear response curve is obtained. This curve can accurately describe the changes in equipment wear under different processing parameter combinations. Based on the performance degradation data of the target scheduled processing equipment, the precision retention capability evaluation model and Bayesian algorithm are used to analyze the precision retention capability data of the target scheduled processing equipment. The performance degradation data is searched in the parameter library. The precision retention capability evaluation model adopts a neural network model. Naive Bayes is used to select and extract features at the input layer of the precision retention capability evaluation model. The resulting feature vector is input into the model for prediction, thereby obtaining the precision retention capability data of the processing equipment. Equipment status detection is performed based on the equipment status feature vector, equipment wear response curve, and precision retention capability data. The equipment wear changes under the target processing process information are determined based on the equipment status feature vector and equipment wear response curve. The state changes of the target scheduled processing equipment are analyzed based on the equipment wear changes and precision retention capability data, thus obtaining equipment status detection information.

[0026] S15: adjusting the target processing information based on the equipment status detection information to obtain adjusted target processing information; In the specific implementation process of the present invention, the target processing technology information is adjusted based on the equipment status detection information to obtain the adjusted target processing technology information, including: performing adjustment step analysis on the target processing technology information based on the equipment status detection information to obtain adjustment step information; performing coupling effect analysis using an adaptive coupling regulator based on the adjustment step information to obtain coupling effect analysis data; adjusting the target processing technology information based on the processing energy consumption model and the processing time model using the coupling effect analysis data to obtain the adjusted target processing technology information.

[0027] Specifically, the target processing information is analyzed for adjustment step length based on the equipment status detection information, that is, the direction in which each parameter in the target processing information should be increased or decreased and the adjustment amplitude are analyzed based on the equipment status detection information to obtain the adjustment step length information. Based on the adjustment step length information, the coupling effect analysis is performed using an adaptive coupling regulator. The adaptive coupling regulator selects one of several parameter types of the processing information as the research parameter type, and uses the research parameter type as the independent variable. For example, the cutting speed is selected as the independent variable, and the remaining parameter types are used as quantitative variables. 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 processing quality can be observed and analyzed. Each time a different parameter type is selected to repeat the above process, the mutual influence and coupling effect between the parameters are analyzed, and the coupling parameter groups whose coupling effect index is greater than the preset coupling effect index are identified, that is, the parameter changes in these combinations have a significant impact on the control target, and at the same time, for each coupling The coupling effect relationship of the coupled parameter group is marked. Based on the coupling parameter group and the coupling effect relationship mark, the adjustment logic of the adaptive coupling regulator is designed. The logic should be able to automatically identify and respond to the coupling effect according to the current processing conditions and parameter status. The adjustment step information is input into the adaptive coupling regulator. The adaptive coupling regulator performs a coupling effect analysis between several parameters of the processing technology 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 processing process under the current adjustment step. The adaptive coupling regulator generates a coupling effect, that is, obtains coupling effect analysis data. The coupling effect analysis data quantifies the strength and direction of the coupling effect between the parameters under the current adjustment step. Based on the processing energy consumption model and the processing time model, the target processing technology information is adjusted using the coupling effect analysis data to determine whether the coupling effect analysis data is consistent with the expected target. If the coupling effect analysis data is inconsistent with the expected target, such as resulting in a decrease in processing efficiency, a reverse parameter adjustment is performed, that is, the adjustment step information is adjusted according to the coupling effect analysis data to reduce or eliminate the adverse coupling effect, until the deviation between the obtained coupling effect analysis data and the expected target meets the preset deviation threshold, the adjustment step information is used as the final adjustment step. If the coupling effect analysis data is consistent with the expected target, the adjustment step information is directly adjusted. The step length information is used as the final adjustment step length. The processing energy consumption model analyzes the energy consumption of furniture manufacturing after adjusting the target processing technology information through the adjustment step length. The processing time model analyzes the time of furniture manufacturing after adjusting the target processing technology information through the adjustment step length. The deviation between energy consumption and time and the preset threshold value is calculated, and the adjustment step length is adjusted according to the deviation. The target processing technology information is adjusted through the adjusted adjustment step length to obtain the adjusted target processing technology information, so that the obtained adjusted target processing technology information takes into account the state changes of the processing equipment while taking into account the processing energy consumption and processing time.

[0028] S16: 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 to obtain optimized target processing technology information, and control the target scheduling processing equipment to manufacture furniture according to the optimized target processing technology information.

[0029] In the specific implementation process of the present invention, the 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 simulation results to obtain the optimized target processing technology information, including: inputting the adjusted target processing technology information into the simulation software to perform furniture manufacturing simulation simulation to obtain the furniture manufacturing simulation simulation results; extracting feedback data based on the furniture manufacturing simulation simulation results, and optimizing the adjusted target processing technology information based on the feedback data to obtain the optimized target processing technology information.

[0030] Specifically, the adjusted target processing technology information is input into the simulation software to perform furniture manufacturing simulation to obtain the furniture manufacturing simulation results; feedback data is extracted based on the furniture manufacturing simulation results, that is, the furniture manufacturing simulation results are compared with the expected results, and the deviation from the expected results is analyzed to obtain feedback data, and the adjusted target processing technology information is optimized based on the feedback data, and the corresponding optimization value is matched according to the feedback data, and the adjusted target processing technology information is optimized according to the corresponding optimization value to obtain the optimized target processing technology information, further ensuring the reliability of the processing technology information, and controlling the target scheduling processing equipment to perform furniture manufacturing according to the optimized target processing technology information, so that the processing and manufacturing of furniture is more reliable.

[0031] Example 2 See also Figure 2 , Figure 2 FIG. 4 is a flow chart of a processing information analysis and optimization method for furniture manufacturing in another embodiment of the present invention, the method comprising: S201: Decomposing furniture model data of target furniture in the furniture manufacturing plan to obtain parts data of the target furniture; S202: Predicting machining deformation using residual stress analysis based on initial machining process information determined by the part data and the 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; S203: Performing a processing equipment scheduling analysis based on the target processing technology information to obtain target scheduling processing equipment; S204: Preprocessing the operation data collected when the target scheduled processing equipment operates according to the target processing process information to obtain preprocessed operation data; S205: Perform feature extraction and feature dimensionality reduction on the pre-processed operating data to obtain a device state feature vector; S206: 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 a device wear response curve; S207: 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; S208: Performing device status detection based on the device status feature vector, the device wear response curve, and the accuracy retention capability data to obtain device status detection information; S209: Adjusting the target processing information based on the equipment status detection information to obtain adjusted target processing information; S210: 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 to obtain optimized target processing technology information, and control the target scheduling processing equipment to manufacture furniture according to the optimized target processing technology information.

[0032] Example 3 See also Figure 3 , Figure 3 : 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, the device comprising: Data splitting module 31: used to split the furniture model data of the target furniture in the furniture manufacturing plan to obtain the parts data of the target furniture; Processing information correction module 32: used to predict processing deformation using residual stress analysis based on initial processing information determined by part data and cutting temperature model, obtain processing deformation prediction information, and correct the initial processing information based on the processing deformation prediction information to obtain target processing information; Equipment scheduling analysis module 33: used to perform processing equipment scheduling analysis based on target processing technology information to obtain target scheduling processing equipment; Equipment status detection module 34: 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; Information adjustment module 35: used to adjust the target processing information based on the equipment status detection information to obtain the adjusted target processing information; Information optimization module 36: 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.

[0033] In the specific implementation process of the present invention, the specific implementation method of the device item can refer to the implementation method of the above-mentioned method item, which will not be repeated here.

[0034] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the processing information analysis and optimization method for furniture manufacturing described in any of the above-described embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, a storage device includes any medium that stores or transmits information in a readable form by a device (e.g., a computer or mobile phone), and may include a read-only memory, a magnetic disk, or an optical disk.

[0035] Example 4 See also Figure 4 , Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention.

[0036] The embodiment of the present invention further provides an electronic device, such as Figure 4 As shown, 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. It will be understood by those skilled in the art that Figure 3The electronic devices shown do not constitute a limitation on all devices and may include more or fewer components than shown, or combinations of certain components. The memory 41 can be used to store the computer program 42 and various functional modules, and the processor 43 runs the computer program 42 stored in the memory 41, thereby executing various functional applications and data processing of the device. The memory can be internal memory or external memory, or include both internal memory and 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, floppy disk, ZIP disk, USB flash drive, magnetic tape, etc. The processor 43 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, a single-chip microcomputer, or processor 43, or any conventional processor. The processor and memory disclosed in the present invention include but are not limited to these types of processors and memories. The processor and memory disclosed in the present invention are only examples and not limitations.

[0037] 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 are configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the processing information analysis and optimization method for furniture manufacturing in any of the above-mentioned embodiments. For the specific implementation process, please refer to the above-mentioned embodiments and will not be repeated here.

[0038] In addition, the above is a detailed introduction to a processing information analysis and optimization method and related devices for furniture manufacturing provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

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 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; 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 equipment status detection of the target scheduling processing equipment is carried out to obtain the 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 method includes: performing machining deformation prediction using residual stress analysis based on initial machining process information determined by 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. Determining a machining quality target based on the part data, and determining preliminary machining process information based on the machining quality target; Based on the preliminary processing information, the least square method is used to construct a cutting temperature model, and the linear weighted method is used to analyze the optimization objective function of the preliminary processing information based on the cutting temperature model. Based on the optimization objective function, the preliminary processing information is adjusted to obtain the initial processing information. Based on the initial processing information, the finite element model of the machined part is used to perform residual stress analysis to obtain the target residual stress; Predict machining deformation based on target residual stress to obtain machining deformation prediction information; The initial machining process information is modified based on the machining deformation prediction information to obtain the target machining process information.

4. 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.

5. The processing information analysis and optimization method for furniture manufacturing according to claim 1, characterized in that: The device status detection of the target scheduling processing equipment based on the target processing technology information combined with the response surface modeling to obtain the device status detection information includes: Preprocessing the operation data collected when the target scheduling processing equipment is operated according to the target processing process information to obtain preprocessed operation data; Perform feature extraction and dimension reduction on the pre-processed operating data to obtain the equipment status feature vector; Response surface modeling was performed based on central composite experimental design to obtain a response surface model, and fuzzy analysis was performed based on the response surface model to obtain the equipment wear response curve; Based on the performance degradation data of the target scheduling processing equipment, the accuracy retention capability evaluation model and Bayesian algorithm are used to analyze the accuracy retention capability data of the target scheduling processing equipment; The equipment status is detected based on the equipment status feature vector, the equipment wear response curve and the accuracy retention capability data to obtain the equipment status detection information.

6. 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.

7. 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.

8. 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 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; 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 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; 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.

9. 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 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which, when executed on an electronic device, enable the electronic device to execute the processing information analysis and optimization method for furniture manufacturing according to any one of claims 1 to 7.

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