Study on shape deviation and crack of ultra-thin glass molding process for curved surface
Source abstract
Abstract
Abstract The demands towards high precision and surface quality of ultra-thin glass for curved screens are continuously rising in the field of smart mobile terminals. Although the ultra-thin glass molding process (UTGMP) has the advantage of the shorter production cycle and higher efficiency, there are still typical forming defects in the molding process, namely crack, shape deviation, and large surface roughness. This paper aimed to investigate the influence mechanism of UTGMP molding temperature and pressure on the shape deviation, crack area, and surface quality of ultra-thin glass. In this study, a finite element model (FEM) was established to study typical forming defects of curved surfaces, and the effects of molding temperature and pressure on the shape deviation and crack area for ultra-thin glass were studied by the FEM simulation method. The simulation results revealed the molding temperature has a significant effect on the shape deviation, crack area and surface quality, while the molding pressure is only strongly correlated with shape deviation and crack area. In addition, the reliability of the model was verified by a series of five-level single factor experiments, and the shape deviation and crack area of ultra-thin glass were discussed in detail. Under the appropriate molding pressure and temperature range (0.45 MPa, 802–806 °C), the accuracy of curvature was improved by 33%, the roughness was reduced by 21%, and the probability of crack was also reduced. Thus, this study contributes to improving UTGMP's molding accuracy and reducing molding defects, and plays a positive role in reducing production costs and improving production efficiency.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Surface Polishing Techniques · Surface Roughness and Optical Measurements · Laser Material Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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