The dynamic mechanical behavior of vermicular graphite cast iron GJV450 under high temperature and high strain rate was studied. The Jmurc constitutive equation was fitted and verified by the temperature up to 600C and the strain rate up to 10000s’. The cutting force, cutting temperature, tool wear and surface integrity of PCBN tool in high speed milling vermicular graphite cast iron were studied. The BP neural network optimized by ant colony algorithm is used to optimize the milling parameters of vermicular graphite cast iron.
1. The dynamic mechanical behavior of vermicular graphite cast iron GJV450 is studied, and the constitutive equation of G_JV450 material is established and verified by experiments.
The main contents are as follows: (1) the dynamic mechanical behavior of GJV450 is studied. The results show that there is obvious thermal softening effect in GJV450 material, and the thermal softening effect corresponding to different strain rates remains unchanged, and there are strain rate strengthening effect and strain rate softening effect at different temperatures, and the effect of strain rate on mechanical properties is not strong.
(2) the jmurc constitutive equation of G “V450 at high temperature and high strain rate is established, and the cutting simulation is carried out to verify the effectiveness of the constitutive equation.
2. The cutting force, cutting temperature, tool wear and surface integrity of PCBN tool in milling vermicular graphite cast iron are studied.
The main results are as follows: (1) the cutting force changes obviously with the feed, which is basically consistent with the cutting temperature. Under the condition of small feed rate, the cutting force and cutting temperature increase with the increase of cutting speed, but the change is not obvious at large feed rate.
(2) the tool failure form of PCBN high speed milling vermicular graphite cast iron is flank wear, and the tool life decreases with the increase of feed rate and speed, but the material removal volume under the condition of large feed rate is larger than that under the condition of small feed rate.
(3) the tool wear mechanism of high speed milling vermicular graphite cast iron with PCBN cutter is mainly bond wear and diffusion wear.
(4) the cutting speed has little effect on the machined surface morphology and surface roughness, while the feed rate has a great influence on the machined surface morphology and surface roughness.
(5) the microhardness increases with the increase of feed rate, and increases continuously with the increase of cutting speed under the condition of small feed, but under the condition of large feed, due to the resistance of thermal softening effect and the influence of tool wear, as a result, the microhardness increases at first and then decreases and then increases with the increase of cutting speed.
3. The BP neural network model is designed, and the training process of BP neural network optimized by ant colony algorithm is emphasized. Due to time constraints, there are two aspects in this study that need to be further studied.
The main results are as follows: (1) in the actual cutting process, especially in the process of high-speed cutting, the strain rate of the material can reach more than 20000s-1, so it is necessary to continue to study the dynamic mechanical behavior of the material at higher strain rate.
(2) further study the influence of many kinds of PCBN tool structure on cutting, such as blade material, tool tip arc radius, chamfering and so on.