Qualitative characterization of residual stress of injection molded polycarbonate goggles based on photoelasticity and digital image processing technique

Abstract

Transparent polymeric materials have gained significant popularity as replacements for glass in various industries due to their low cost, lightweight nature, and high processing efficiency. Injection molding is the primary method for producing transparent polymer parts. However, residual stress often poses a challenge, leading to various defects. Traditional approaches utilize photoelasticity and polarizers to determine stress in transparent parts, which costs time and cannot be easily used for online monitoring and real time quality inspection. The digital image processing (DIP), combined with photoelasticity, offers a promising solution for detecting residual stress and assessing product quality in real-time during manufacturing. In this study, we propose a photoelastic digital image processing (PDIP) approach that combines photoelasticity and DIP techniques to identify residual stress and evaluate part quality using a single digital polarized image without the need for a rotation process. By collecting and analyzing the gray values and variations from the photoelastic images through PDIP, we compared and correlated the gray values of the entire image, a specific area on one side lens, a warp line, and a weft line. Additionally, numerical simulations were performed to validate the proposed method. The results demonstrated the feasibility of this instant identification method. The PDIP technique should be applied to a specific area or line within the parts. By obtaining the average gray value, the instantaneous identification of residual stress can be achieved. The determination of the specific area or line can be tailored according to the quality requirements of the parts.