Fine mold accessories have the following requirements:
1. Cold and hot fatigue resistance
Some molds are in the state of repeated heating and cooling in the working process, which makes the cavity surface subject to tension and pressure stress, causes the surface crack and spalling, increases the friction, hinders the plastic deformation, reduces the dimensional accuracy, and causes the mold failure. Hot and cold fatigue is one of the main failure modes of hot working die. This kind of Dongguan fine die parts should have high cold and hot fatigue resistance.
2. Strength and toughness
The working conditions of Dongguan fine die parts are mostly very bad, some often accept a larger impact load, which leads to brittle fracture. In order to avoid the sudden brittle fracture of die parts, the die should have high strength and toughness.
The toughness of the die mainly depends on the carbon content, grain size and microstructure of the data.
3. High temperature performance
When the working temperature of the die is higher, the hardness and strength will drop, leading to early wear or plastic deformation of the die and failure. Therefore, the die data should have high tempering stability to ensure that the die has high hardness and strength at working temperature.
4. Corrosion resistance
When some molds, such as plastic molds, work, because of the presence of chlorine, fluorine and other elements in the plastic, HCI, HF and other strong corrosive gases are synthesized and precipitated after heating, which corrodes the surface of the mold cavity, increases its surface roughness, and aggravates the wear failure.
5. Fracture properties
During the working process of Dongguan fine die parts, fatigue fracture often occurs under the long-term effect of cyclic stress. The modes of fatigue fracture are repeated impact fatigue fracture, tensile fatigue fracture, contact fatigue fracture and bending fatigue fracture.
The fatigue fracture property of the die mainly depends on its strength, toughness, hardness and the content of inclusions in the data.