Forging production is an important link in machinery manufacturing industry, and the optimization of forging process is of great significance for improving forging quality, reducing production cost and improving production efficiency. The purpose of this paper is to discuss the method and practice of forging process optimization in forging production, so as to provide reference for actual production.
In the forging process optimization of forging production, the predecessors mainly studied the influence of forging temperature, deformation speed, deformation amount and other process parameters on the quality and performance of forging. The results show that the mechanical properties and microstructure properties of forgings can be effectively improved with reasonable forging process parameters. However, in actual production, due to the limitations of equipment, materials, environment and other factors, forging process often can not reach the ideal state. Therefore, it is necessary to optimize the forging process for the specific problems in actual production.
The method of experimental research and data analysis is used to optimize the forging process in forging production. Firstly, according to literature review and actual production experience, the forging process parameters that need to be optimized are determined. Then, the experimental scheme is designed to observe the change of forging quality and performance by changing forging process parameters. Finally, statistical analysis of the experimental data is carried out to find the best combination of forging process parameters.
The experimental results show that the forging temperature has the most significant effect on the quality and performance of forging parts in the process parameters such as forging temperature, deformation speed and deformation amount. Under proper forging temperature, the mechanical properties and microstructure properties of forgings can be significantly improved. In addition, the deformation speed and deformation amount also have a certain impact on the quality and performance of the forging. By adjusting these parameters, the forging process can be optimized effectively.
According to the experimental results and the actual production situation, the following forging process optimization scheme is proposed:
Control forging temperature: According to the specific production requirements and material characteristics, determine the best forging temperature range. In actual production, the performance of the heating equipment and insulation materials can be controlled to ensure that the raw materials reach the appropriate temperature before forging.
Adjust the deformation speed and deformation amount: according to the specific production requirements and material characteristics, determine the best deformation speed and deformation amount. In actual production, the deformation speed and deformation amount can be effectively controlled by adjusting the parameters of forging equipment and the skill level of operators.
Optimization of forging process: According to the specific production requirements and material characteristics, the forging process is optimized. For example, advanced forging technologies such as multi-directional forging and isothermal forging can be used to improve the mechanical properties and organizational properties of forgings. At the same time, through the optimization of forging process, the production cost can be reduced and the production efficiency can be improved.
Strengthen quality control: While optimizing the forging process, quality control measures should also be strengthened. For example, non-destructive testing, chemical composition analysis and other means can be used to detect and evaluate forgings to ensure that the quality of forgings meets the requirements. At the same time, unqualified products should also be traced and dealt with to avoid similar problems from happening again.
The optimization method and practice of forging process in forging production are studied in this paper. Through experimental research and data analysis, the best forging process parameter combination is found out, and the corresponding optimization scheme and practical experience are put forward. However, due to the complexity and diversity in actual production, forging process optimization still faces many challenges. Future research directions include further improving forging process optimization methods, studying the application of new materials and new processes in forging production, and developing intelligent forging equipment.
