26 results about "Taguchi methods" patented technology

The invention discloses a method for optimizing pressing rivet quality based on grey correlation and a factor analysis method, and relates to the technical field of pressing rivet quality optimization. Firstly, a taguchi method is adopted, a press riveting quality test model with busbar design parameters and control parameters of an automatic squeeze riveter as design optimization variables and with push-out force and twisting force as responses is constructed, a grey correlation method is adopted, the correlation degree in all responses is analyzed, dimension reduction is conducted on multiple indexes in combination with a factor analysis method, weights of different indexes are obtained, and a press riveting quality test model is obtained. Reducing multiple indexes into a single index, then calculating a weighted grey correlation degree, carrying out comprehensive evaluation, finally obtaining an optimal parameter level combination, and analyzing a factor with the maximum influence degree on the indexes. According to the method, the factors which have the most influence on the pressing rivet quality can be quickly selected from the numerous parameters, and when the pressing rivet quality is evaluated by the push-out force and the twisting force in the pressing rivet quality evaluation indexes, the problems of information overlapping of multiple evaluation indexes and the like can be solved.

The invention provides a multi-physical field optimization design method and system for a high-capacity high-frequency transformer, and belongs to the technical field of high-frequency transformers, and the method comprises the steps: constructing optimization targets of the cost, power density and efficiency of the high-frequency transformer according to the application demands of the high-frequency transformer; based on the optimization targets of the cost, the power density and the efficiency of the high-frequency transformer, a calculation model of iron core loss and winding loss is introduced, and the loss amount of the high-frequency transformer is obtained; based on the maximum feasible size of the high-frequency transformer iron core and thermal and electromagnetic constraints, introducing iron core size constraints; and based on the constraint of the size of the iron core, carrying out optimization design on the high-frequency transformer by using a Taguchi method, and based on a selective non-dominated genetic algorithm, carrying out Pareto frontier relation analysis on the high-frequency transformer subjected to the optimization design to obtain an optimal design scheme of the high-frequency transformer. And comprehensive optimization requirements of multiple physical fields can be effectively met.

The invention discloses a permanent magnet synchronous motor multi-objective optimization method fusing an equivalent magnetic network method and a Taguchi method, and relates to the technical field of permanent magnet synchronous motor optimization. The method comprises the following steps: step 1, establishing an equivalent magnetic network model of the permanent magnet synchronous motor; 2, determining an optimization target of optimization design under the fusion of the equivalent magnetic network method and the Taguchi method; 3, determining an optimization factor according to the influence factors of the optimization target; step 4, determining an optimization level according to the selected optimization factor and arranging an orthogonal test; step 5, calculating each scheme by using an equivalent magnetic network method according to an orthogonal test to obtain calculation results of different schemes; 6, analyzing data after calculation results of different schemes are obtained; and 7, determining an optimal control factor combination and an optimal control solution according to data analysis results of different schemes. According to the method, the requirements of multi-target simultaneous optimization and rapid calculation can be met, and the optimization design level and the calculation efficiency of the permanent magnet synchronous motor are effectively improved.

The invention belongs to the technical field of ocean engineering, and particularly discloses a multi-objective optimization design method for an ocean vehicle based on hierarchical design, which comprises the following steps of: S1, defining design variables, optimization objectives and constraint conditions, and constructing a parameterized model of the vehicle; s2, sensitivity analysis is conducted on the design variables, and the design variables are divided into high-sensitivity design variables and low-sensitivity design variables according to sensitivity ranking; s3, processing the high-sensitivity design variables by a multi-objective optimization method based on adaptive sequential sampling to obtain an optimal combination of the high-sensitivity design variables; s4, optimizing the low-sensitivity design variables by utilizing a fuzzy reasoning Taguchi method to obtain an optimal combination of the low-sensitivity design variables; and completing optimization of the aircraft according to the optimal combination of the high-sensitivity design variables and the optimal combination of the low-sensitivity design variables. According to the method, through technical innovation such as variable layering, adaptive sampling and staged optimization, the bottleneck of a traditional modeling and optimization method on high-dimensional complex problems is effectively broken through, and the method has clear technical advantages and wide engineering adaptability; and reliable and efficient technical support is provided for subsequent parameter sensitivity analysis, uncertainty quantification and rapid design iteration.

The present invention relates to a device and method for supporting optimal control of an injection molding machine to suppress the generation of hazardous gases in the injection molding machine. More specifically, by applying the Taguchi Method, gas collection data is used as a characteristic value, a signal-to-noise ratio (SNR) of the characteristic value is obtained according to a feature vector of control parameters, and a model for predicting the SNR using the feature vector is prepared. Among a plurality of control parameter data to be used in the process, optimal control parameter data is recommended according to the SNR predicted by the model, and is then applied to the process performance of the injection molding machine, thereby minimizing variations in gas generation and reducing the total amount of gas produced, such that the generated gas can be stably purified before discharge. This technology was developed as part of the Regional Intelligence Innovation Talent Training Program, supported by the Institute for Information & Communication Technology Planning & Evaluation and with funding from the Ministry of Science and ICT, Republic of Korea (RS-2024-00436765).

This invention proposes a multi-physics field optimization design method and system for large-capacity high-frequency transformers, belonging to the technical field of high-frequency transformers. The method includes: establishing optimization targets for high-frequency transformer cost, power density, and efficiency based on the application requirements of the high-frequency transformer; introducing calculation models for core loss and winding loss based on these optimization targets to obtain the loss of the high-frequency transformer; introducing a core size constraint based on the maximum feasible size of the high-frequency transformer core and thermal and electromagnetic constraints; optimizing the high-frequency transformer design based on the core size constraint using the Taguchi method; and performing a Pareto front relationship analysis on the optimized high-frequency transformer using a selective non-dominated genetic algorithm to obtain the optimal design solution for the high-frequency transformer. This method can effectively address the comprehensive optimization needs of multiple physical fields.

The present invention discloses a robotic arm inverse kinematics device that integrates the Taguchi method with an improved artificial bee colony algorithm. After completing the kinematic modeling of the robotic arm, the method needs to apply the kinematic model and constraints to construct a fitness function for the problem, then use the improved artificial bee colony algorithm to search for the inverse kinematics solution of the robotic arm, and finally use the Taguchi method to screen the important parameters of the improved artificial bee colony algorithm. The method introduces a new intelligent swarm algorithm, an improved algorithm of the artificial bee colony algorithm, to solve the inverse kinematics problem of the robotic arm. By considering the swarm movement speed and introducing relevant parameters, the algorithm's solution accuracy for the problem is effectively improved, and the shortcoming of falling into local optimality in solving the robotic arm inverse kinematics problem is improved. At the same time, applying the Taguchi method to select the most appropriate algorithm parameters can obtain better solution results.

The invention particularly relates to a two-stage fingertip sealing structure optimization method based on a Taguchi method, and belongs to the technical field of mechanical sealing. According to the method, the leakage rate, the inter-stage pressure difference rate and the highest temperature serve as optimization targets, six key structure parameters including the first-stage fingertip seal shoe height Xg1, the second-stage fingertip seal shoe height Xg2, the rear baffle height Hb1, the rear baffle height Hb2 and the fingertip sheet layer number N1 and N2 are selected as control factors, five levels are set for each factor, and an L25 (65) orthogonal test table containing 25 test combinations is constructed. FLUENT fluid simulation software is used for simulating each test combination, corresponding performance indexes are obtained, and through mean value analysis and variance analysis, a control factor which has the largest influence on each response target and an optimal level combination of the control factor are determined. The accuracy of the optimization result can be ensured, the optimization efficiency of the structural parameters is improved, and the result is accurate and reliable; the two-stage fingertip seal optimization design method based on the Taguchi method is adopted, and the sealing performance of the two-stage fingertip seal can be effectively improved; the test frequency is greatly reduced, and a large amount of test time and cost can be saved.

The invention provides a thermal storage performance optimization method and device based on solid-state hydrogen storage, and relates to the technical field of solid-state hydrogen storage, and the method comprises the steps: constructing a solid-state hydrogen storage device finite element model containing heat-mass-momentum transmission multi-physics field coupling; analyzing the influence level of the to-be-optimized parameters of the spiral heat exchange tube on the heat storage performance by adopting a Taguchi method; and generating a Pareto frontier solution set through a non-dominant sorting second-generation genetic algorithm, constructing a multi-target evaluation matrix based on weighted grey relational degree analysis, and performing secondary screening by adopting an approximate ideal solution sorting method to determine an optimal parameter combination. According to the method, a multi-physics field coupling simulation model is constructed, a two-stage optimization architecture of a non-dominant sorting second-generation genetic algorithm and an approximate ideal solution sorting method is adopted, and collaborative optimization of spiral pipe parameters and fluid dynamic parameters is achieved. Key factors are pre-screened through a Taguchi method, a three-dimensional evaluation matrix is established in combination with grey correlation analysis, and balanced optimization of reaction scores and outlet temperature is ensured.

The invention discloses a method for improving the reliability of a wheel spoke and rim assembly, and relates to the technical field of wheels. The method comprises the steps that control factors and noise factors of the wheel spoke and rim assembly are collected; performing primary screening on the control factors and the noise factors by using single factor analysis to remove redundant factors; designing an orthogonal simulation scheme based on the control factor to identify a main effect and an interaction effect of the control factor; optimizing the simulation analysis process of the control factor and the noise factor by using a Taguchi method based on the main effect and the interaction effect of the control factor so as to reformulate an orthogonal simulation scheme; and constructing a target function and analyzing an optimal factor group in combination with an orthogonal simulation scheme. According to the method, the reliability, the cost and the light weight of the wheel assembly are comprehensively optimized, and the consumption of computing resources is reduced.

This application provides a stator and rotor optimization design method for a permanent magnet synchronous motor, including: Step S1, establishing a parametric electromagnetic model based on the main dimensions and performance targets of the permanent magnet synchronous motor, and selecting key design variables; Step S2: conducting orthogonal experimental design on the key design variables based on the Taguchi method, and using finite element simulation to obtain the motor efficiency, average torque, and torque ripple coefficient under different parameter combinations; Step S3: refining the rotor structure based on the motor basic design established by the optimal parameter combination; Step S4: performing mechanical strength verification at the highest operating speed and thermal performance verification under rated operating conditions; Step S5: generating an efficiency characteristic spectrum of the motor within the full operating range and outputting manufacturing drawings. This application, through this method, ensures that the motor achieves a comprehensive performance of high efficiency and low vibration and noise while meeting strict dimensional constraints.

The present invention relates to the technical field of chip packaging processes and discloses a collaborative optimization method for chip DFN packaging and processes based on machine learning. The method determines quality indicators of solder paste type, PAD solder layer thickness, chip lower surface solder layer thickness, and chip material type through a percentage-based weighted evaluation method of a Taguchi orthogonal test and a range analysis method of a signal-to-noise ratio. The range value R of each factor is sorted and analyzed to obtain a comprehensive score. The optimal DFN packaging parameter combination of the Taguchi method is further optimized through random forest machine learning and grid search. The reflow soldering temperature curve is also optimized to select the optimal reflow soldering temperature curve. The method solves a series of problems caused by unreasonable material combination or structural design in existing chip DFN packaging. The method greatly improves optimization efficiency and also improves the accuracy of Taguchi method optimization parameters to a certain extent.

The invention relates to the technical field of motor optimization design, and particularly discloses a hybrid excitation permanent magnet motor optimization method for improving a Taguchi method and electronic equipment, the hybrid excitation permanent magnet motor optimization method for improving the Taguchi method comprises the following steps: defining an optimization model, determining design parameters, an optimization range and an optimization target of a motor, and establishing a parameterized model; sensitivity analysis and parameter layering, wherein the design parameters are divided into sensitive parameters and non-sensitive parameters through sensitivity analysis; and performing sequential optimization on the non-sensitive parameters, performing staged optimization on the non-sensitive parameters according to the sensitivity priority, and reducing the design space. According to the hybrid excitation permanent magnet motor optimization method for improving the Taguchi method, layered optimization, noise factor quantification and fuzzy multi-target fusion are combined for the first time, the limitation that a traditional Taguchi method only depends on a static experiment is broken through, an efficient and robust solution is provided for complex motor design, and the method is suitable for large-scale popularization and application. And the method has remarkable technical progress and industrial popularization potential.

Optimizing furniture structure based on a grey Taguchi method is provided. Relevant design parameters and evaluation indexes are determined, and orthogonal tests using a Taguchi method are designed an

The invention relates to the technical field of integrated circuit packaging, and discloses a 3D-IC packaging injection molding reliability prediction method based on SVM-MOGA collaborative optimization, and the method comprises the steps: firstly, employing a Taguchi method in combination with finite element analysis to determine a group of optimal structure parameters; then based on the structure, multiple sets of process parameter samples are generated by adopting an optimal Latin hypercube sampling method, and a data set containing process parameters and quality indexes is established through finite element simulation; then, objective weights of all the quality indexes are determined through a CRITIC weight method, and machine learning agent models such as a support vector machine (SVM) are constructed for fitting and screening; and finally, in combination with the objective weight, performing global optimization on the optimal agent model by adopting a multi-objective genetic algorithm (MOGA), and bringing an optimization result back to the finite element model for closed-loop verification. According to the method, iterative finite element simulation is replaced by constructing the machine learning agent model, and global optimization is carried out in combination with the multi-target intelligent algorithm, so that efficient prediction of the optimal process parameter combination is realized.

The present application relates to a kind of ship ac-dc hybrid power system probabilistic power flow calculation method based on Taguchi method, by introducing the Taguchi in quality engineering, with ac-dc hybrid power flow calculation combination forms ship ac-dc hybrid probabilistic power flow calculation method, can further reduce the number of calculations, improve calculation efficiency;Through ship ac-dc hybrid probabilistic power flow calculation can master each node state in system, and then carry out power distribution and improve the economy of system;The method of the present application is not limited to fixed network structure, with wide applicability and good extensibility, can be applied to different types of ships even land transport tools.

The invention discloses an intelligent generation method and system for a fault mode library of a new energy electric vehicle, and the method comprises the following steps: constructing a triple knowledge model, and determining product information and a mapping relation between a product function and a fault mode; generating a knowledge graph based on the triple knowledge model; selecting a plurality of graph evaluation factors based on node types in the knowledge graph; performing orthogonal verification according to the plurality of atlas evaluation factors and preset factor level parameters, and outputting a factor level pair corresponding to the optimal signal-to-noise ratio as an optimal test set; performing knowledge graph correction based on the optimal test set; generating a fault mode library based on the corrected knowledge graph; according to the method, based on the triple knowledge model, the product reliability knowledge chain is constructed, the knowledge graph is generated, the coverage rate and correctness of the knowledge graph are calculated by using the Taguchi method, the knowledge graph is corrected according to the sampling result, and repeated iteration is performed until the given correct rate requirement is met, so that the fault mode library of the new energy electric vehicle is generated.

The application discloses a kind of nanocrystalline high-frequency transformer multi-physics field multi-objective robust optimization design method and system, belong to high-frequency transformer technical field, method includes: constructing multi-objective optimization function;Introduce the coupling calculation model of core loss and winding loss;Establish multidimensional constraint condition;Identify the high sensitivity parameter and low sensitivity parameter that optimization target is significantly influenced, simplify optimization dimension;Construct Kriging surrogate model, generate sample point using Latin hypercube sampling;Determine the optimization of Kriging surrogate model, obtain Pareto optimal solution set;Divide control factor and noise factor, use Taguchi method to design orthogonal experiment;Introduce support vector regression to construct robust optimization surrogate model, optimize control factor in combination with NSWOA algorithm.The application uses the above method and system, improves the anti-interference ability of equipment, solves the performance dispersion problem under high-frequency working condition, guarantees the stability of nanocrystalline high-frequency transformer long-term operation.

The invention discloses a reactance parameter robustness optimization method and system for an alternating current excitation motor. The method comprises the following steps: S1, obtaining an alternating current excitation motor design scheme for determining slot matching, pole pair number, rated voltage, rated capacity, stator slot type, rotor slot type and winding based on engineering preset requirements; s2, establishing a deterministic motor reactance parameter optimization model based on the AC excitation motor reactance parameters; s3, based on the deterministic motor reactance parameter optimization model and the manufacturing tolerance of the motor size parameters, establishing a robustness optimization model of the motor reactance parameters; step S4, using an AC excitation motor to design normal distribution conforming to size parameters and a Taguchi method to generate noise; and S5, optimizing the robustness optimization model of the motor reactance parameter based on a noise and optimization algorithm, outputting a motor reactance parameter robustness optimization scheme, and completing the reactance parameter robustness optimization method of the AC excitation motor.

The invention discloses a permanent magnet brushless direct current motor with a non-uniform air gap and a parameter optimization method thereof. The permanent magnet brushless direct current motor comprises a stator core, a stator winding, a rotor core, permanent magnets, lightening holes and a rotating shaft. The non-uniform air gap is located between the stator and the rotor and is determined by the outer contour of the circular arc surface of the rotor, the outer contour of the circular arc surface of the rotor is composed of two parts including a segmented arc part and a valley-shaped non-segmented arc part, the segmented arc part is composed of eight segmented arcs, and the valley-shaped non-segmented arc part is composed of a valley-shaped non-segmented arc part and a valley-shaped non-segmented arc part. The valley-shaped non-segmented arc part is formed by superposing outer contours of third harmonics and sine waves, the two parts are sequentially connected to form an outer contour of the rotor, and parameters of the non-uniform air gap are optimized through a Taguchi method. Compared with the prior art, the sine degree of an air-gap magnetic field can be effectively improved, the torque pulsation of the permanent magnet brushless direct current motor is reduced, the harmonic content is reduced, and therefore the efficiency of the motor is improved.

An ammonia / diesel dual-fuel engine combustion process optimization method based on CFD simulation and a Taguchi method belongs to the technical field of internal combustion engines, and comprises the following steps: parameterizing a combustion chamber structure of a dual-fuel engine, obtaining characteristic points of the combustion chamber, carrying out single-factor influence rule analysis on each parameter, determining a combustion system parameter value range, and determining a combustion system parameter value range; selecting a proper level number, designing an orthogonal table according to a Taguchi method, and formulating a test scheme; establishing a corresponding engine model in CFD simulation software by using the designed orthogonal table, carrying out analogue simulation, and obtaining emission and thermal efficiency data under different parameter combinations; extracting emission and thermal efficiency data in a simulation result, calculating a signal-to-noise ratio of each factor under each level by using a signal-to-noise ratio calculation formula, and determining an optimal parameter combination; through range analysis, the influence degree of each factor on the performance index is evaluated, main influence factors are identified, and the optimization direction is defined; and adjusting and optimizing parameter setting according to an analysis result, and verifying the effectiveness of the optimization scheme by using CFD simulation software again. The problems that an ammonia / diesel oil dual-fuel engine is low in combustion efficiency and complex in combustion parameter optimization process are solved, and support can be provided for development of the high-efficiency dual-fuel engine.

The invention provides an all-solid-state battery charging strategy optimization method based on a Taguchi method, and the method comprises the steps: determining the charging stages of a to-be-tested all-solid-state battery and the current candidate value of each charging stage according to preset charging boundary conditions and preset charging multiplying power of interface impedance, current, charging time and battery temperature; designing a target orthogonal array list based on the charging stage, the current candidate value and the preset level number; performing charging and discharging test on the to-be-tested all-solid-state battery by adopting each row of charging strategy combination in the target orthogonal array list to obtain battery parameters; and calculating the average signal-to-noise ratio of each charging stage under each level according to the battery parameters and the initial interface impedance, and combining the current candidate values under the highest average signal-to-noise ratio in each charging stage to obtain a target charging strategy. According to the all-solid-state battery charging strategy of the scheme, the interface stability and the charging efficiency can be both considered, the research and development cost is reduced, and the reliability and the cycle life adaptability of the charging strategy are improved.

The application belongs to the technical field of engineering design optimization, and discloses a double-layer iterative optimization scheme determination method and system with variable number of variables, which comprises the following steps: determining an optimization object, setting an outer-layer iteration variable as the number of variables of different optimization objects, and setting an inner-layer iteration variable as the variable itself of different optimization objects; performing outer-layer iteration: determining a variable number scheme by using an orthogonal table, performing iteration one by one in the inner layer according to the variable number scheme, obtaining an optimal comprehensive score value and variable value under the current variable, and returning all inner-layer iteration results to the outer layer; the outer layer compares the comprehensive score values to obtain an optimal variable number and variable value of this iteration of the outer layer; and repeating the inner-layer and outer-layer iteration until a convergence condition is met to output an optimal scheme. The orthogonal table in the Taguchi method is used to reasonably reduce the number of tests and reduce the cost consumption caused by multiple iterations, so that the application is a feasible and practical optimization method for improving engineering efficiency.

The application relates to a double-rotor flux switching motor optimization design method for a manned aircraft, which comprises the following steps: determining the initial size of the motor and establishing a motor electromagnetic field model to perform electromagnetic performance analysis, calculating the no-load back electromotive force and harmonic distortion rate; selecting an optimization factor and an optimization level to establish an orthogonal table, performing orthogonal test according to the orthogonal table by using the Taguchi method, selecting the optimization factor combination to optimize the no-load back electromotive force and the harmonic distortion rate, and obtaining structure parameters considering multiple targets; calculating the motor output torque ripple and establishing a permanent magnet hybrid segmented model, selecting optimization parameters to construct a response surface mathematical optimization model, and analyzing to obtain the final value of the optimization parameters. The double-rotor flux switching motor designed by the application has high torque density, high reliability and high torque overload capacity, and the rotor structure is simple, the loss is small and easy to dissipate heat, and the power-to-weight ratio of the motor of the power system of the manned aircraft is improved.

The present invention discloses a non-pneumatic wheel with a negative Poisson's ratio (NPR-R) structure, an optimization method, and a related system. Based on the parameters of the NPR-R non-pneumatic wheel, a relative density model, a peak force (PCF) model, and a unit mass energy absorption (SEA) model are established for the NPR-R wheel. A multi-objective optimization mathematical model is established, targeting the minimum relative density, minimum PCF, and maximum SEA of the NPR-R wheel, with the NPR-R wheel's structural parameters as design variables. This multi-objective discrete robust optimization design method, combining the continuous Taguchi method, improved grey relational analysis (IGRA), and principal component analysis, is used to form a multi-objective discrete robust optimization method. The multi-objective optimization mathematical model is then optimized to determine the NPR-R wheel's structural parameters. This method not only enhances the safety and stability of all-terrain vehicles during driving but also improves the impact resistance of NPR-R non-pneumatic tires.

This invention relates to an adaptive optimization method for 3D DIC parameters based on image quality assessment, belonging to the field of machine vision 3D measurement technology. The method involves: detecting and optimizing the synchronization of multiple cameras to ensure reliable data acquisition; conducting trial acquisitions based on multiple initial parameter combinations and quantitatively evaluating the images using image quality assessment indicators, including image signal-to-noise ratio, image entropy, image contrast, speckle distribution uniformity, average speckle size, and average image grayscale gradient; employing a phased optimization strategy, using the Taguchi method to screen the optimal value range of key parameters, and iteratively optimizing within this range using Bayesian optimization until the optimal parameter combination that meets the requirements is obtained and output. This invention, by constructing an "evaluation-optimization" closed loop, transforms 3D DIC acquisition parameters from "manual trial and error" to "system optimization," improving the automation level and reliability of the measurement results.