In the field of mechanical and engineering, fasteners are a key connection element, and their performance and quality are crucial to the impact of the entire system. With the continuous progress of science and technology, higher requirements are put forward for the design and optimization of fasteners. In order to improve the performance and reduce the cost of fasteners, a fastener optimization design strategy based on response surface model has been widely concerned. This paper will introduce the basic concept, implementation method and application effect of this strategy.
The optimal design strategy of fasteners is of great significance in improving product performance and reducing manufacturing cost. Traditional fastener design methods often focus on a single index such as strength and stability, while ignoring the influence of other factors. The optimal design strategy based on response surface model can realize the optimal design of fasteners by establishing a comprehensive and systematic optimization model and considering multiple factors comprehensively.
The optimal design strategy of fasteners based on response surface model is mainly to establish a mathematical model to describe the relationship between design variables and objective functions. The model takes into account a number of factors, including material properties, geometric dimensions, and surface treatment. In the process of establishing the model, through the experimental design and data analysis, the interaction between various factors is determined, and mathematical methods are used to fit the model. Finally, the optimization algorithm is used to solve the model iteratively, and the multi-objective optimal design scheme is found.
The steps to implement the fastener optimization design strategy based on response surface model are as follows:
Determine design variables: Select design parameters that have a significant impact on fastener performance as design variables, such as material type, diameter, length, thread spacing, etc.
Determine the objective function: According to the design requirements, determine the objective function that needs to be optimized, such as minimizing weight, maximizing strength, minimizing cost, etc.
Experimental design: According to the design variables and objective function, develop the experimental scheme, including the experimental purpose, experimental method, experimental process, etc.
Data collection and analysis: Obtain data through experiments, and use statistical analysis methods to analyze the data, including descriptive statistics, correlation analysis, regression analysis, etc.
Build response surface model: Based on experimental data and analysis results, use mathematical methods to build response surface model, such as polynomial regression model, neural network model, etc.
Optimization algorithm selection and solution: Select suitable optimization algorithms, such as genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, etc., iteratively solve the response surface model, and find the design scheme that meets the multi-objective optimal.
Scheme implementation and verification: The optimized design scheme is applied in actual production, and the performance verification and evaluation are carried out to confirm the effectiveness and feasibility of the optimized design scheme.
Taking the optimization design of an automobile wheel hub bolt as an example, the application effect of the fastener optimization design strategy based on response surface model is illustrated. In this case, design variables include bolt material type, diameter, length, thread spacing, etc. The objective function is to minimize weight and maximize strength. Through experimental design and data analysis, a polynomial regression model is established to describe the relationship between the design variables and the objective function. Finally, genetic algorithm is used to solve the model iteratively, and a multi-objective optimal design scheme is found. After optimization, the weight of bolts is reduced by 20%, and the strength is increased by 15%.
The fastener optimization design strategy based on response surface model has obvious advantages in fastener design, which can comprehensively consider multiple factors, achieve multi-objective optimization, improve product design performance and reduce manufacturing cost. However, this strategy also has some limitations, such as the complexity of experimental design and data analysis, and the error of model fitting. The future research and application of this strategy can be carried out from the following aspects:
Improve the experimental design and data analysis methods: improve the fitting effect and reliability of the response surface model by improving the experimental scheme and improving the data acquisition accuracy;
Research on more efficient optimization algorithms: Explore and develop more efficient optimization algorithms to improve optimization speed and accuracy;
Expand the application field: the strategy is applied to more types of fastener design and optimization to expand its application scope;
Combined with modern advanced technology: combined with modern computer technology, artificial intelligence, machine learning and other technologies, to achieve the optimization of fastener design automation and intelligence.
In short, the fastener optimization design strategy based on response surface model provides a new idea and method for the design and manufacture of fasteners, which has important theoretical and application value. With the continuous progress of technology and the continuous expansion of application fields, this strategy will play an increasingly important role in the future design and manufacturing of fasteners.
