The calculation simulation and optimization design of the forging die of construction machinery refers to the process of designing, analyzing and improving the forging die of construction machinery by using computer simulation technology. The following are the main steps of computational simulation and optimization design:
- Forging parameter modeling: According to the geometric shape, material and process requirements of the parts, CAD software is used to model the forging. The geometry of forging includes information such as lines, surfaces and volumes.
- Material mechanical properties modeling: Select the appropriate material and model its mechanical properties. This includes the material strength, toughness, plasticity and other mechanical property parameters.
- Process modeling: According to the geometric shape and material characteristics of the forging, combined with the forging process rules, the forging process in the process of modeling, including prefabrication, heating, forging, cooling, etc.
- Finite element analysis: Using finite element software to simulate and analyze each process step of forging. By analyzing the stress, deformation and temperature field in the process, the performance of forging under different process conditions is evaluated.
- Optimization design: According to the results of finite element analysis, through parameter adjustment and structural optimization, improve the performance of forging. This includes reducing stress concentration areas during the process, reducing deformation, and improving material utilization.
- Result evaluation: Conduct finite element analysis on the optimized design again to verify whether the improved performance meets the requirements. Based on the evaluation results, we can determine whether we need to continue to optimize the design.
Through calculation simulation and optimization design, it can effectively improve the performance of construction machinery forging die, optimize the process flow, reduce production cost, improve product quality and reliability. At the same time, this method can also reduce the test and improvement cycle in the actual manufacturing process, and improve the control and predictability of the process.
