The aim of the topological optimization design of construction machinery forging die is to optimize the combination of construction machinery forging die through scientific methods and technical means, so as to improve the forming quality and production efficiency of the product. The following will introduce a genetic algorithm based on the construction machinery forging die combination topology optimization design method.
- Demand analysis: First of all, it is necessary to clarify the design requirements and manufacturing needs of construction machinery forgings, including forging process parameters, mold materials and other requirements.
- Model building: According to the forging geometry, strength requirements, assembly requirements and other characteristics, establish the corresponding mathematical model and finite element analysis model.
- Optimization objective determination: According to the specific needs and optimization principles, determine the optimization objective of the mold combination. Common optimization goals include minimizing deformation, reducing the number of molds, and improving forming quality.
- Genetic algorithm design: Genetic algorithm is used as an optimization algorithm, and the optimization search of mold combination is realized by designing fitness function, genetic operator and genetic operation.
- Individual representation and coding: The mold combination scheme is transformed into the individual expression form in the genetic algorithm, and binary coding is performed.
- Definition of fitness function: According to the model analysis results and optimization objectives, the fitness function is designed to evaluate the fitness of each individual. The fitness function can be set according to specific problems, such as the amount of deformation, stress distribution, and forming quality.
- Genetic operator design: including selection, crossover and mutation operations. The selection operation sorts the individuals through the fitness function and selects the individuals with higher fitness as the parents of the next generation. Cross operation produces a new generation of individuals by exchanging chromosome fragments of individuals. Mutation operations introduce new genes by altering certain bits of an individual’s chromosome, increasing the diversity of the population.
- Iterative solution: According to the set termination conditions, such as reaching the maximum number of iterations or convergence error less than the preset threshold, the genetic algorithm is used for multiple iterations to continuously search for the optimal mold combination scheme.
- Simulation verification: finite element analysis and forming process simulation will be carried out on the optimized mold combination scheme to evaluate its performance and feasibility, and compare and analyze with the traditional design scheme.
- Result analysis and optimization scheme selection: According to the simulation analysis results, evaluate the pros and cons of various mold combination schemes, and select the best design scheme.
Through the above method, the forging efficiency and forming quality can be improved effectively, and the production cost and material waste can be reduced. This method can comprehensively consider the complex shape of forging, forming difficulty and assembly requirements, and provide more efficient and accurate mold design scheme for construction machinery industry.
